Tag Archives: least privilege

Balancing speed and safety: A control framework for AI coding agents

Post Syndicated from Daniel Begimher original https://aws.amazon.com/blogs/security/balancing-speed-and-safety-a-control-framework-for-ai-coding-agents/

AI coding agents are part of the developer toolchain. Tools like Kiro and Claude Code generate features, tests, and code refactors from natural-language prompts. A single agent can open dozens of pull requests (PRs) across your repositories in an afternoon. That productivity comes with a trade-off: agents optimize for task completion at machine speed with no understanding of your organization’s risk.

Through protocols like the Model Context Protocol (MCP), agents also reach beyond the integrated development environment (IDE) to call APIs, query databases, and modify infrastructure and even entire environments, expanding the scope of resources your application security team defends.

This post lays out an application security (AppSec) control framework for AI coding agents. Two pillars organize the framework: author-time controls shape what the agent produces in the IDE; build-time controls verify and gate what reaches production. Your existing secure software development lifecycle (SDLC) controls still apply and are critical to a defense-in-depth security strategy. The framework shows where to layer additional guardrails so AppSec scales with agent-driven development. The framework is tool-agnostic and cloud-agnostic. Throughout, we use AWS services—Kiro in the IDE and AWS CodePipeline in the build—as a running example that you can adapt to your own toolchain.

Risks

Each of the following risks includes a treatment summary. The control framework section later in this post provides implementation details. The risks are ordered by severity with the highest impact risks first.

R001. Prompt and context injection

Agents read untrusted content, such as issue descriptions, web pages, MCP responses, and README files in third-party packages. Text from outside parties can redirect the agent to disclose secrets, open unauthorized PRs, or invoke tools without user consent. This risk, known as prompt injection, is the top risk in the OWASP Top 10 for LLM Applications. Any agent that reads content from outside parties is exposed, with or without MCP, so connecting tools widens the scope of impact.

Treatment: Treat non-developer input as untrusted. A large language model (LLM) can’t reliably separate instructions from data in a single context window, so architect for it: keep the agent that orchestrates trusted actions separate from the one exposed to untrusted content and grant the exposed agent only read-only, least-privilege access. Require human approval for irreversible actions. Use version-control steering files to prevent silent tampering.

R002. Inadvertent data disclosure and overly permissive configurations

Agents optimize for getting work done. Left unchecked, the code they generate can default to wildcard identity and access management policies, open security groups, and unencrypted storage, or embed sensitive values in code rather than referencing a secrets manager. Most coding agents now include safety mechanisms that make these outcomes less likely, but they remain imperfect, so you still need controls to account for the possibility.

Treatment: Security requirements in a steering document, plus policy-as-code scanning (Checkov, cfn-nag) in the IDE and pipeline. See Context as a security control.

R003. Uncontrolled changes reaching production

Ungated code reaching production isn’t new, but AI agents amplify it. Machine-speed generation can propagate a flawed pattern across repositories before it’s identified.

Treatment: Branch protection rules requiring PR approval (a human-in-the-loop checkpoint), pre-commit hooks for security checks, and sandboxed agent runs that prevent direct pushes to protected branches. The right balance between human review and automated speed depends on the risk profile of the change. For many low-risk paths, automated checks alone might suffice, while higher-risk changes warrant a human checkpoint.

R004. Supply chain risks

Agents don’t always distinguish current best practices from outdated patterns. They might recommend deprecated packages, reference library versions with new Common Vulnerabilities and Exposures (CVEs), and hallucinate package names that don’t exist, which can introduce risks of dependency confusion issues.

Treatment: Software Composition Analysis (SCA) in the pipeline (for example, Amazon Inspector code scanning or Dependabot) to flag vulnerable or unexpected dependencies. For additional control, resolve against a scoped registry like AWS CodeArtifact. Even without a fully curated registry, lockfile validation and allow-listing critical packages reduce exposure.

R005. Uncontrolled external access

Through MCP and tool integrations, agents query databases, call APIs, and modify infrastructure. Without constraints on which tools and data an agent can reach, a single misconfigured integration provides unintended access to sensitive resources.

Treatment: Scope MCP servers to least-privilege tools and resources, enforce authn or authz on external connections, and audit tool invocations. The control point is the configuration file. Review it the same way you review AWS Identity and Access Management (IAM) policies.

R006. Hallucinations and incorrect code

Agents produce plausible-looking output. Code that compiles, passes linting, and looks reasonable can still be functionally wrong: misusing APIs, introducing subtle logic errors, or implementing security-sensitive operations incorrectly. Code that passes continuous integration (CI) but is wrong slips through review; code that fails to build is caught immediately.

Treatment: Layer deterministic verification (static application security testing (SAST), unit tests) with non-deterministic review (LLM-assisted screening against the specification). Neither catches everything alone.

R007. Scope creep

Given a bug-fix prompt, an agent might also refactor surrounding code, disable an unreliable test, or reorganize imports. Unrequested changes introduce regressions and complicate review.

Treatment: A reviewed specification document that defines what must change and what must not, paired with a targeted review of the proposed changes. See Specifications as scope boundaries.

The preceding risks share a common thread: agents produce output faster than humans can review it, and they lack context to self-correct.

The following framework addresses this gap. It organizes controls into two pillars: author-time (pre-generation and post-generation of code) and build-time (in the pipeline, before code reaches production). Author-time controls shape what the agent produces. Build-time controls verify it. Neither is sufficient alone; together they reduce the volume and severity of issues that reach human reviewers.

Deterministic compared to non-deterministic mitigations

Deterministic mitigations [D] produce the same result every time. Linters, SAST scanners, secrets detection, and policy-as-code match patterns against rules and define security invariants: no critical findings, no hardcoded secrets, and no wildcard IAM policies. Use them when the condition can be expressed as a rule. Organizations already have these and must continue enforcing them.

Non-deterministic mitigations [ND] use model judgment. They include steering documents, LLM-as-judge review, specification compliance checks, and scope-creep detection, and they evaluate intent rather than patterns. They catch novel issues that rules miss, but are probabilistic. Use them when evaluation requires context or reasoning across files. This is the new layer that AI-generated code demands, because agents produce code that can pass every deterministic check yet remain functionally wrong.

Human review [H] provides the final layer for the risk-based decisions neither tool type can make. Apply it where judgment is needed, not everywhere: routing every change to a person invites consent fatigue, where reviewers approve by reflex and the control loses its value. The default reflex is to route everything back to a human, but that isn’t always the right response—reserve human judgment for the decisions that genuinely need it.

The control framework

The framework organizes controls into two pillars. Author-time controls (Pillar 1) shape what the agent produces in the IDE, before code is generated and just after. Build-time controls (Pillar 2) verify and gate that output in the pipeline, before it reaches production. The controls within each pillar are tagged deterministic [D], non-deterministic [ND], or human [H].

Pillar 1: Author-time controls (pre- and post-generation of code)

Author-time controls work inside the IDE, where the developer and agent still hold full context. They shape the prompt and the generated output before it ever reaches a pull request. The following controls apply at this stage.

Context as a security control [ND]

Control statement: Encode security invariants as natural-language constraints in a steering document that every developer environment consumes at session start. Addresses R002.
Many AI coding agent risks share one root cause: the agent lacks the security context an experienced developer carries implicitly. Your security team sets the policies, such as Amazon Simple Storage Service (Amazon S3) buckets require encryption, API gateways require mutual TLS, and credentials must come from AWS Secrets Manager. Developers don’t always have these requirements available when they’re building. They build what works, not what’s compliant. An AI agent amplifies this gap because it defaults to whatever pattern dominated its training data, with no awareness of your organization’s security posture.

A key mitigation is steering. Security teams write these invariants once as natural-language guidance in a steering document, then distribute them as shareable resources that developers consume in their IDE. The agent loads the file at session start and treats the contents as standing requirements:

  • IAM policies must follow least-privilege principles; no wildcard Amazon Resource Names (ARNs).
  • No hardcoded credentials in source code; use a secrets manager.
  • Security groups must not allow unrestricted inbound access.

This shifts security left, before code generation begins. Steering biases generation toward secure defaults; it doesn’t guarantee them. Treat it as a strong default, paired with the following deterministic gates that block non-compliant code from merging. Security teams define the rules once and every developer environment inherits them automatically. Steering reduces the volume of issues that reach the pipeline, though it doesn’t replace downstream scanning.

How to write effective steering rules: Keep each rule specific and testable, scope it to a concrete risk class, keep the rule set concise so the agent can hold it in context, and iterate from the issues your scanners and reviewers surface.

Specifications as scope boundaries [ND]

Control statement: Require a reviewed specification before code generation begins. Define what must change and what must not. Addresses R007.

Spec-driven workflows turn vague prompts into reviewable specifications before code is generated. This creates a human checkpoint at the design phase, where security decisions are made:

  • Requirements use testable notation that’s auditable before the agent writes a line of code. For example, the Easy Approach to Requirements Syntax (EARS): WHEN [condition] THE SYSTEM SHALL [behavior].
  • Tasks are ordered in implementation steps, each mapped back to a requirement.

For bug fixes, specifications add a critical element: unchanged behavior documentation. This is an explicit list of behaviors that must continue working, giving the agent a written boundary against scope creep.

In this model, the specification becomes the primary artifact, code is a derivative of it. Human review effort concentrates on whether the specification solves the right problem with the right constraints, not on reading implementation diffs line by line.

Controlled tool access using MCP [D + ND]

Control statement: Scope each MCP server to the minimum set of tools the agent needs, and give it a dedicated, scoped-down credential rather than the developer’s own. Maintain an allowlist of reviewed MCP servers. Addresses R005.

MCP servers act as controlled gateways between the agent, the external tools, and data:

  • Dependency management – An MCP server fronting your private package registry resolves dependencies against curated packages, not the public internet. This is a deterministic constraint on supply chain risk.
  • Infrastructure tooling – Visibility into current resource configurations prevents templates that conflict with existing infrastructure.
  • Scoped permissions – Each MCP server exposes a defined set of tools and resources. You choose exactly what the agent can access, supporting least-privilege at the integration layer. You supply that credential through the agent’s configuration (in Kiro, the env block of .kiro/settings/mcp.json). Avoid autoApprove: ["*"], which removes the human approval prompt on every tool call.

IDE code scanning [D]

Control statement: Run real-time static analysis in the IDE so security issues surface while the developer (and agent) still have full context. Addresses R002, R006.

Real-time diagnostics catch syntax errors, type mismatches, and configuration issues as the developer types. A malformed IAM policy is flagged before the agent builds further on it. Security-focused extensions (ESLint security plugins, Checkov, SAST) layer on top for immediate feedback while code is fresh in context.

Hooks: Automated guardrails at the point of action [D + ND]

Control statement: Attach deterministic checks to file-save events and non-deterministic verification to task-completion events. Addresses R002, R007.

  • Shell command hooks [D] – Triggered on file save, these run a linter, formatter, or security scanner and produce the same result every time. They enforce hard rules.
  • AI-powered hooks [ND] – Triggered on task completion. These prompt the agent to verify that the implementation matches the specification and check for any untested edge cases or files that were modified outside the task’s scope.

Pillar 2: Build-time controls (in the pipeline)

Build-time controls run in the pipeline after code is committed and before it reaches production. They verify and gate what the agent produced, catching what author-time controls did not. The following controls apply at this stage.

Layered security scanning [D]

Control statement: Run secrets detection, static analysis, dependency scanning, and infrastructure-as-code scanning in sequence. Fail the build on any critical finding. Addresses R002, R003, R004.

  1. Secrets detection runs first because it’s cheapest and addresses a high-severity class of issue. It scans for hardcoded API keys, database connection strings, and credentials that AI agents might inadvertently include.
  2. SAST scans source code for injection issues, insecure deserialization, and resource leaks. Custom rules can target AI-specific anti-patterns including overly broad exception handling, deprecated APIs, placeholder credentials, dynamic code execution through eval().
  3. Software Composition Analysis (SCA) identifies known CVEs in dependencies. This is critical for AI-generated code, which might reference deprecated packages or hallucinate package names that open you to dependency confusion issues.
  4. Infrastructure as code (IaC) scanning validates AWS CloudFormation, Terraform, and AWS Cloud Development Kit (AWS CDK) templates against security policies before deployment. Catches overly permissive IAM roles, unencrypted storage, and public-facing resources the agent created.

Each stage halts the pipeline on failure. Results export to a standard format (Static Analysis Results Interchange Format (SARIF)) for compliance auditing and flow downstream to human reviewers. The open source Automated Security Helper (ASH) bundles secrets, SAST, SCA, and IaC scanners behind one command that you can run locally and in AWS CodeBuild, emitting SARIF for the gates that follow.

Quality gates [D]

Control statement: Define pass/fail thresholds for each scan type. Block deployment on any critical or high-severity finding. Addresses R003.

Quality gates convert scan results into go/no-go decisions. Define thresholds for each severity: block on critical findings, require justification for highs, and track mediums. The gate is deterministic: if a threshold is breached, the pipeline stops. Exceptions require documented approval.

Differentiate blocking compared to advisory modes: hard failures on main, advisory on feature branches. Avoid gates becoming a friction that teams route around.

AI-assisted review [ND]

Control statement: Use an LLM reviewer to pre-screen every pull request for specification compliance, scope creep, and security anti-patterns before human review. Addresses R001, R006, R007.

  • Specification compliance – Does the implementation match the requirements document?
  • Scope verification – Were files modified outside the task’s stated scope?
  • Security pattern review – Are there logic errors, misused APIs, or insecure patterns that pass SAST but violate intent?

This pre-screening focuses human reviewer attention on genuine risks rather than formatting or obvious issues. On AWS, AWS Security Agent (code review in preview at publication) checks pull requests against AWS-managed and custom security requirements. The reviewer screens and surfaces findings; the merge decision stays with a human.

A critical principle: the agent that wrote the code should not be the agent that reviews it. A separate session helps avoid self-confirmation bias, but a separate session alone doesn’t always avoid the generator’s blind spots, because two sessions of the same model can share them. Where practical, use a different model for review so the reviewer is less likely to inherit the same systematic weaknesses.

Human-in-the-loop review [ND + H]

Control statement: Require human approval on most pull requests, especially those touching security-sensitive or high-blast-radius code. Lower-risk changes might be eligible for agent-assisted or fully automated approval as tooling matures. Provide reviewers with scan results, LLM pre-screening output, and specification context to enable fast, informed decisions. Addresses R003.

Scale review depth to the risk of the change. Low-risk or boilerplate changes can take a lighter-touch review, while security-sensitive or novel-logic changes warrant mandatory deep review and a second reviewer.

Scanners catch known patterns but can’t judge whether code implements the intended business logic. Human review also serves to calibrate trust: teams build intuition about where agents excel (boilerplate, test writing) and where they’ve tended to struggle (novel business logic, security-sensitive operations), recognizing that this frontier shifts as models improve.

Place two approval gates: after security scans (reviewer focuses on correctness and business logic, with scan results as context) and before production deployment (final sign-off after integration testing). Treat human review as a secondary control, not a guarantee: reviewers are themselves non-deterministic and can miss issues, so human review layers on top of the deterministic gates rather than replacing them.

Putting the framework into practice on AWS

The framework is tool-agnostic, but AWS gives you building blocks for each pillar. The following services map directly to the controls described previously: Kiro for author-time guardrails, and CodeBuild and CodePipeline for build-time gates.

Kiro: Structured AI development

Kiro maps to Pillar 1: It puts the author-time controls in the IDE, where the developer and agent still share full context. Each feature in the following list implements one of those controls, configured in-repo under .kiro/ so the guardrails are version-controlled and shared across the team rather than set per developer.

  • Steering documents – Markdown files in .kiro/steering/ load into the agent’s context at session start. Conditional inclusion using fileMatch (for example, ["**/*.tf"]) loads IaC-specific rules only when relevant.
  • Specification-driven workflows – Three-phase specifications (requirements in EARS, design, and tasks) with review checkpoints. Bug-fix specifications capture unchanged behavior explicitly.
  • Agent hooks – Triggered on file save, tool invocation, or task completion. Shell hooks run deterministic checks (linters, tests); Ask Kiro hooks run AI prompts for non-deterministic review. For example, a security pre-commit scanner hook can flag hardcoded credentials when the agent finishes a task.
  • Property-based testing – Guided by a specification or hook, Kiro can generate property-based tests (for example, using the hypothesis library) that exercise hundreds of randomized inputs, probing edge cases a hand-written test suite would miss.
  • MCP integrations – Connect Kiro to private package registries, internal docs, issue trackers, and infrastructure tooling, creating the controlled tool access pattern.

For enterprise environments, Kiro supports AWS IAM Identity Center for single sign-on and provides IP indemnity coverage for subscribers. Check the Kiro documentation for current Region availability.

AWS CodeBuild and AWS CodePipeline: Pipeline controls

CodeBuild runs each scanning tool (checking for secrets, SAST, SCA, and IaC) as a build action. A non-zero exit code fails the action, and the stage halts or rolls back according to its OnFailure setting. Findings export as SARIF to Amazon S3 for compliance, and CodePipeline action variables pass results to downstream approval actions.

  • CodeBuild exit codes halt the pipeline on scan failures
  • AWS Lambda invoke actions evaluate scan results against configurable thresholds and return pass/fail decisions
  • Manual approval actions halt the pipeline, send Amazon Simple Notification Service (Amazon SNS) notifications, and link to review artifacts; decisions and reviewer identity are logged for audit

The following table consolidates the framework into a single view that includes each stage of the SDLC and the deterministic [D] and non-deterministic [ND] controls that apply there. Every stage carries both, a reminder that neither control type is sufficient on its own.

Stage Deterministic [D] Non-deterministic [ND]
IDE (pre-generation) Steering files loaded Steering documents, specification-driven constraints
IDE (post-generation) Shell hooks: Linter, formatter, type checker, and secrets scan AI-powered task completion hooks, context constraints
Pull request SAST, SCA, and IaC scanning LLM PR pre-screening and scope verification
Pipeline (pre-deploy) Full security scan suite, integration tests, and policy-as-code AI-assisted review for human approvers
Post-deploy Runtime monitoring and anomaly detection AI-powered incident triage

Conclusion

This post laid out a framework for adopting AI coding agents at machine speed without letting unreviewed risk reach production. It layers guardrails at two points:

  • Author-time controls – Steering, specs, and scoped tools shape what the agent generates in the IDE.
  • Build-time controls – Scanning, quality gates, and layered review verify it before it reaches production.

No single layer is enough: deterministic gates enforce hard rules, non-deterministic review catches what they miss, and human judgment is reserved for the decisions that need it. Together, they let AppSec scale with agent-driven development.

Where to start this week:

  1. Start with steering and specs – Encode security requirements as steering and use specifications for new features. Highest impact, lowest effort. For a ready-made starting set, the open source Project CodeGuard (a Coalition for Secure AI project under OASIS Open, of which Amazon is a contributing member) publishes reusable steering rules for common risk classes—hardcoded credentials, IaC misconfiguration, supply chain, and MCP security—that you can adapt to your AWS environment.
  2. Add deterministic pipeline gates – Integrate SAST, SCA, and secrets detection. Table-stakes regardless of AI usage.
  3. Calibrate and iterate – Review what controls catch, adjust steering for recurring issues, and expand agent autonomy as trust builds.
  4. Accountability – Developers remain accountable for the security of what they ship. AI agents accelerate development; they don’t transfer ownership.

More information:

If you have feedback about this post, submit comments in the Comments section below.


Daniel Begimher

Daniel Begimher

Daniel is a Senior Security Engineer at AWS, where he built and shipped the company’s first customer-facing AI security agent. He created SIR-Bench, a benchmark for measuring how deeply AI incident-response agents investigate before acting, and Automated Security Helper (ASH), an open source scanner. He co-leads application security technical field community at AWS, and speaks at conferences including AWS re:Invent, re:Inforce, and Cyber Week.

Danny Cortegaca

Danny Cortegaca

Danny is a Principal Security Specialist Solutions Architect and co-leads the Application Security focus area within the AWS Security and Compliance Technical Field Community. He joined AWS in 2021 and partners with some of the largest organizations in the world to help them navigate complex security and regulatory environments. He loves talking about application security with customers and has helped many adopt threat modeling into their practices.

Enforce least-privilege authorization in multi-agent AI chains using Cedar

Post Syndicated from Dhananjay Karanjkar original https://aws.amazon.com/blogs/security/enforce-least-privilege-authorization-in-multi-agent-ai-chains-using-cedar/

If you’re building multi-agent AI systems, you need to prevent authorization scope from silently expanding as agents delegate tasks through multi-hop chains. Without proper controls, an agent can potentially act beyond what the originating user authorized, even when role-based access control (RBAC) policies are in place. The OWASP Top 10 for Agentic Applications classifies this risk as ASI03: Identity & Privilege Abuse.

This post shows you how to address the potential risk using a three-layer policy model built with Cedar, an open source authorization policy language, deployed on Amazon Web Services (AWS). The reference implementation uses OAuth 2.0 for authentication and Cedar for authorization. A trusted identity provider authenticates the originating user, then Cedar policies enforce authorization across three layers using verified token claims.

Reference implementation overview

To enforce authorization at each hop in a multi-agent delegation chain, the reference implementation uses two AWS Lambda functions in sequence. A Model Context Protocol (MCP) adapter Lambda function normalizes inbound requests and cryptographically signs the originating user context. This prevents downstream tampering. A Cedar evaluator Lambda function evaluates three independent policy layers sequentially, halting on the first deny.

Table 1: Three-layer Cedar policy evaluation model

Layer What it checks Principal to resource
L1 – Agent-to-tool Whether the invoking agent has a sufficient trust score (1–5), belongs to the correct namespace (for example, payments), and is in the production lifecycle stage Agent to tool
L2 – Agent-to-agent delegation Whether the delegation hop count is within the hard limit of five, and whether requested tasks are a subset of the target agent’s registered capabilities Agent to agent
L3 – Originating user authorization Whether the human who initiated the chain has the required role (for example, admin), has completed MFA, and is within the allowed delegation depth Agent to tool (user in context)

Architecture

Cedar evaluates authorization but doesn’t establish identity. Before Cedar can evaluate context.originating_user.role or context.originating_user.mfa_verified, a trusted authentication layer must establish the user’s identity and produce verifiable claims. Steps 1–3 handle authentication; steps 4–10 handle authorization. The architecture shown in Figure 1 is described in the following lists:

Authentication (steps 1–3)

  1. The originating user authenticates with an OIDC-compliant identity provider (in this reference implementation, Amazon Cognito with TOTP multi-factor authentication (MFA)). The identity provider (IdP) issues a signed JSON Web Token (JWT) containing claims such as sub, role, amr (authentication methods), and session_id.
  2. Amazon Cognito returns the signed JWT to the user.
  3. The user passes the JWT and task request to the AI agent (MCP client). The agent carries the originating user context in the MCP _meta envelope.

Authorization pipeline (steps 4–10)

  1. The AI agent sends a Model Context Protocol (MCP) request to AWS WAF, which filters using CommonRuleSet, SQLiRuleSet, rate limiting, and body size constraints.
  2. Amazon API Gateway (with Amazon Cognito authorizer) verifies the JWT signature against the user pool’s public keys and rejects invalid or expired tokens. Valid requests are forwarded to the MCP protocol adapter Lambda function, which applies Amazon Bedrock Guardrails content filtering.
  3. The adapter extracts verified claims from the token and maps them to Cedar context attributes:
    1. JWT role claim : context.originating_user.role
    2. JWT amr includes MFA method: context.originating_user.mfa_verified = true
    3. JWT sub: context.originating_user.user_id
    4. JWT sid: context.originating_user.session_id
    5. JWT amr claim: context.originating_user.authentication_method

    The adapter then computes an HMAC-SHA256 signature over the user context (user_id, role, mfa_verified, authentication_method, and session_id in canonical order) using a key from AWS Secrets Manager.

  4. The adapter constructs a signed request envelope and invokes the Cedar evaluator Lambda function.
  5. The evaluator verifies the HMAC-SHA256 signature, retrieves L2 and L3 Cedar policies from Amazon Verified Permissions, and evaluates all three layers (L1, L2, and L3), halting on the first deny.
  6. The evaluator emits an Open Cybersecurity Schema Framework (OCSF) 99001 audit event to Amazon CloudWatch Logs. Failed emissions fall back to an Amazon Simple Queue Service (Amazon SQS) dead-letter queue (DLQ).
  7. Amazon CloudWatch dashboards and alarms monitor evaluation latency, deny rates, and DLQ depth. Alarm notifications route through Amazon Simple Notification Service (Amazon SNS).

Context integrity through delegation hops

Two mechanisms work together to protect identity across hops:

  • Hash-based Message Authentication Code (HMAC-SHA256) ensures integrity and authenticity. Every downstream evaluator verifies this signature before trusting the context.
  • OAuth 2.0 Token Exchange (RFC 8693) sets delegation scope using the on-behalf-of (OBO) pattern. When the orchestrator delegates to a downstream agent (data-bot), it exchanges the original token for a scoped OBO token that records who’s acting on behalf of whom and with what authority. The Cedar policies (detailed in Step 2: Three-layer policies) then check whether that scoped delegation is permitted and verify the originating user claims carried in the OBO token. Token exchange limits each downstream agent to only the delegated task’s scope instead of passing through the full original token. For enterprise deployments, use token exchange alongside HMAC. OAuth tracks who is acting on behalf of whom and with what scope. HMAC verifies that the context hasn’t been tampered with and came from a trusted source.

Prerequisites

The following prerequisites are needed to deploy the reference implementation. Before you begin, clone the repository:

git clone https://github.com/aws-samples/sample-cedar-agentic-ai-authorization.git
cd sample-cedar-agentic-ai-authorization

Verify that you have the following:

Walkthrough

In this walkthrough, you define the Cedar entity schema and policies, deploy the infrastructure with AWS CDK, and integrate your identity provider.

To define the Cedar entity schema

In this step, you define a schema with two entity types (Agent and Tool) and two actions (invoke_tool and delegate_task) in the AgentAuthz namespace. Notice that there is no User entity. Instead, you carry the originating user’s identity in the evaluation context record, which is a structured data object passed alongside each authorization request.

{
  "AgentAuthz": {
    "entityTypes": {
      "Agent": {
        "shape": {
          "type": "Record",
          "attributes": {
            "trust_level": { "type": "Long", "required": true },
            "namespace": { "type": "String", "required": true },
            "registered_capabilities": {
              "type": "Set", "element": { "type": "String" }, "required": true
            },
            "lifecycle_stage": { "type": "String", "required": true }
          }
        }
      },
      "Tool": {
        "shape": {
          "type": "Record",
          "attributes": {
            "namespace": { "type": "String", "required": true },
            "risk_level": { "type": "String", "required": true }
          }
        }
      }
    },
    "actions": {
      "invoke_tool": {
        "appliesTo": { "principalTypes": ["Agent"], "resourceTypes": ["Tool"] }
      },
      "delegate_task": {
        "appliesTo": { "principalTypes": ["Agent"], "resourceTypes": ["Agent"] }
      }
    }
  }
}

This schema is deployed to an Amazon Verified Permissions policy store by the VerifiedPermissionsStack CDK stack. In the reference implementation, the schema file is located at cedar-entity-schema.json.

Agent topology and attributes

The following tables show the agents and tools registered in this reference implementation, along with the attributes the Cedar evaluator function retrieves from the entity store. The test scenarios that follow trace requests through this topology.

Table 2: Agent attributes

Entity Type trust_level namespace lifecycle_stage registered_capabilities
orchestrator Agent 5 orchestration production

delegate_task

route_request

finance-agent Agent 3 payments production

process_payment

refund

data-bot Agent 4 data production

query_records

delete_records

Table 3: Tool attributes

Tool namespace risk_level
process_payment payments medium
delete_records data high
query_records data low

The orchestrator can delegate to both data-bot and finance-agent. Each agent can only invoke tools within its registered capabilities. The test scenarios below trace requests through these delegation paths.

To create three-layer Cedar policies

The following policies are deployed to the same Verified Permissions policy store. In the reference implementation, policy files are located under cedar/policies/ organized by layer: layer1-agent-to-tool/, layer2-agent-to-agent/, and layer3-originating-user-auth/.

Layer 1 (agent-to-tool): This policy permits the finance-agent to invoke the process_payment tool only when three conditions are met: the agent’s trust score is at least 3, it belongs to the payments namespace, and it’s deployed in the production lifecycle stage. If any condition fails, the request is denied. The agent’s trust_level, namespace, and lifecycle_stage aren’t self-reported in a production deployment. Instead, the evaluator retrieves these attributes from the Verified Permissions entity store using the agent_id as a lookup key.

Important: The reference implementation accepts these values from the request payload for simplicity. Production deployments must validate agent attributes against an authoritative source to prevent a compromised agent from escalating its own trust.

The trust_level attribute uses a 1–5 integer scale that represents an agent’s verified maturity: 1 for newly registered and untested agents, 3 for agents that have passed integration testing and security review, and 5 for agents with a proven production track record. Organizations assign trust levels through their agent promotion pipeline, not through self-declaration. The lifecycle_stage attribute (development, staging, production) prevents pre-production agents from invoking production tools, even if they have the correct namespace and trust score.

// L1-001: Finance agent can invoke payment tools
permit(
  principal == AgentAuthz::Agent::"finance-agent",
  action == AgentAuthz::Action::"invoke_tool",
  resource == AgentAuthz::Tool::"process_payment"
) when {
  principal.trust_level >= 3 &&
  principal.namespace == "payments" &&
  principal.lifecycle_stage == "production"
};

Layer 2 (agent-to-agent delegation) enforces depth limits and capability constraints. The orchestrator agent delegates tasks to data-bot only when the delegation chain is three hops or fewer and the requested capabilities are a subset of data-bot’s registered capabilities. A separate forbid policy (L2-004) enforces a hard system-wide limit of five hops regardless of which agents are involved.

// L2-002: Orchestrator can delegate to data agent
permit(
  principal == AgentAuthz::Agent::"orchestrator",
  action == AgentAuthz::Action::"delegate_task",
  resource == AgentAuthz::Agent::"data-bot"
) when {
  context.delegation_depth <= 3 &&
  context.target_capabilities.containsAll(context.requested_capabilities)
};

Layer 3 (originating user authorization) keeps the agent as the principal, but the policy evaluates context.originating_user to validate the human who initiated the request. data-bot invokes the delete_records tool only when the originating user has the admin role, has verified MFA, and the delegation chain is at most two hops deep. Without this layer, an agent with the right capabilities could invoke destructive tools regardless of who initiated the request.

// L3-001: High-risk tool (delete_records) requires admin + MFA
permit(
  principal == AgentAuthz::Agent::"data-bot",
  action == AgentAuthz::Action::"invoke_tool",
  resource == AgentAuthz::Tool::"delete_records"
) when {
  context.originating_user.role == "admin" &&
  context.originating_user.mfa_verified == true &&
  context.delegation_depth <= 2
};

Key design point: The principal remains the agent, not a user entity. The user’s role and MFA status are checked through context attributes, keeping the schema to two entity types and two actions.

Integrate your IdP

The reference implementation uses Amazon Cognito with TOTP MFA, but most OIDC-compliant providers (Okta, Microsoft Entra ID, Auth0, or AWS IAM Identity Center) work with this pattern. The authentication-to-signing flow is described in the preceding Authentication before authorization section. To use a different IdP, replace the Cognito authorizer on API Gateway with a Lambda or JWT authorizer for your IdP’s issuer URL. Cedar policies remain unchanged.

Deploy the infrastructure with AWS CDK

The reference implementation deploys five CloudFormation stacks: KmsStack, VerifiedPermissionsStack, LambdaStack, SecurityLakeStack, and MonitoringStack. The following commands deploy the stacks in dependency order:

cdk deploy KmsStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy VerifiedPermissionsStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy LambdaStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy SecurityLakeStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy MonitoringStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID

Test the solution

Three end-to-end scenarios validate the evaluation model across different user roles, MFA states, and delegation depths. To run the tests:

  1. Set the API endpoint from the deployment output:
export API_ENDPOINT=$(aws cloudformation describe-stacks --stack-name LambdaStack \
  --query "Stacks[0].Outputs[?OutputKey=='ApiEndpoint'].OutputValue" --output text)

  1. Run the end-to-end tests:
.venv/bin/python -m pytest tests/e2e/ -v -s

The end-to-end tests cover the three scenarios described in the following sections. Each test sends a request through the deployed API and validates the per-layer authorization decisions.

Scenario A: Layer 3 enforcement

A support-role user (no MFA) requests record deletion through orchestrator and data-bot.

Layer Decision Reason
L1: Agent-to-tool PERMIT data-bot has trust level 4, namespace data, and lifecycle production
L2: Agent-to-agent PERMIT orchestrator is authorized to delegate to data-bot, depth within limits
L3: Originating user DENY User role is support, not admin; MFA not verified
Overall DENY Denying layer: L3

Without Layer 3, this request would have been permitted based on agent capabilities alone, demonstrating why originating user authorization is essential.

Scenario B: Authorized admin request

An admin user with MFA requests the same operation through the same chain.

Layer Decision Reason
L1 PERMIT Agent attributes match
L2 PERMIT Delegation path authorized
L3 PERMIT Role is admin, MFA verified, depth is less than or equal to 2
Overall PERMIT All three layers permit

Scenario C: Delegation depth limit

An admin with MFA requests the same operation, but the delegation chain has six hops. This scenario tests the Layer 2 depth constraint independently of user authorization.

Layer Decision Reason
L1 PERMIT Agent attributes match
L2 DENY Depth of six exceeds the hard limit of five
Overall DENY Denying layer: L2 (L3 not evaluated – halt)

Even an authorized admin can’t bypass the delegation depth constraint.

Alignment with the security principles for agentic AI

The AWS Office of the CISO published Four security principles for agentic AI systems. The following table shows how this solution maps to each principle.

Principle How the solution implements it
Secure development lifecycle across components Property-based testing (Hypothesis) for adversarial input fuzzing, Cedar policy formal verification with strict schema validation, end-to-end scenarios testing policy bypass and privilege escalation paths, and infrastructure-as-code (IaC) with AWS CDK.
Traditional security controls remain applicable AWS WAF, Amazon VPC isolation, AWS Key Management Service (AWS KMS) encryption, Amazon Cognito MFA, and Secrets Manager;
NIST SP 800-53 control mapping.
Deterministic external controls (security box) Three-layer Cedar evaluation runs outside the agent’s reasoning loop in a separate Lambda function.
HMAC-signed context prevents tampering.
Verified Permissions (the managed Cedar evaluation service) enforces L2 and L3 at the infrastructure level.
Greater autonomy earned through evaluation trust_level and lifecycle_stage policy attributes calibrate agent capabilities; OCSF 99001 audit events and Amazon CloudWatch dashboards provide the evidence base for expanding autonomy.

Monitoring and audit compliance

Each evaluation produces an OCSF 99001 audit event with request ID, user identity, delegation chain, per-layer decisions, and latency.

The following table maps this implementation to NIST SP 800-53 Rev. 5 controls. Customers are responsible for evaluating whether it meets their compliance requirements.

NIST control Control name How the reference implementation addresses it
AC-4 Information Flow Enforcement User context flows immutably through HMAC-signed envelopes
AC-6 Least Privilege Three-layer evaluation requires both agent capability and user role
AC-6(1) Authorize Access to Security Functions MFA required for high-risk tools in Layer 3
AC-6(5) Privileged Accounts Destructive operations restricted to admin with MFA verified
AU-2 Event Logging Each evaluation is logged as OCSF 99001
AU-3 Content of Audit Records Events include identity, chain, action, resource, decisions, and latency
SI-10 Information Input Validation HMAC verified before evaluation; Amazon Bedrock Guardrails on inbound
IA-2(1) Multi-factor Authentication Layer 3 enforces MFA for high-risk operations
SC-12 Cryptographic Key Management Signing key in Secrets Manager with rotation
SC-28 Protection of Information at Rest Policies in Verified Permissions with STRICT validation

Scaling to multi-account environments

Deploy the Cedar policy store in a central security account and use cross-account IAM roles for workload accounts to call verifiedpermissions:IsAuthorized. Use AWS Organizations service control policies (SCPs) to prevent workload accounts from creating their own policy stores. For standardizing user identity attributes across the organization, consider IAM Identity Center or a centralized OIDC provider that issues consistent claims to your workload accounts. This helps ensure that the context.originating_user attributes are uniform across accounts and agents.

For production deployments, consider extending this pattern with human-in-the-loop escalation for borderline denials, multi-tenant Cedar policy isolation, and Amazon Simple Storage Service (Amazon S3)-backed dynamic policy hot-reload for emergency tool shutdowns.

Clean up

To avoid ongoing charges, delete the deployed resources:

cdk destroy MonitoringStack SecurityLakeStack
cdk destroy LambdaStack
cdk destroy VerifiedPermissionsStack
cdk destroy KmsStack
aws logs delete-log-group --log-group-name /cedar-evaluator/audit  # if RETAIN policy

Conclusion

Multi-agent AI systems need authorization boundaries at every delegation hop. The three-layer Cedar policy model with OAuth 2.0 authentication provides that protection while maintaining least-privilege access. Combining a trusted IdP (AuthN) with Cedar policy evaluation (AuthZ) creates an authorization boundary around each tool invocation, verifying agent capability (L1), delegation path (L2), and originating user authority (L3). The pattern works with an OIDC-compliant IdP and a compute platform that can call Amazon Verified Permissions. Clone the reference implementation and adapt the Cedar policies to your organization’s requirements. For more information, see the Cedar policy language documentation and the Amazon Verified Permissions User Guide.

References

If you have feedback about this post, submit comments in the Comments section below.


Dhananjay Karanjkar

Dhananjay Karanjkar

Dhananjay is a Senior Lead Consultant at AWS Professional Services, specializing in agentic AI systems, multi-agent orchestration, and generative AI security. He holds two US patents and serves as a Responsible AI Champion, with a background spanning financial services, enterprise consulting, and enterprise-scale AI delivery. When not architecting AI solutions, he trains for triathlons, paints oil portraits, and is an avid reader.

Can I do that with policy? Understanding the AWS Service Authorization Reference

Post Syndicated from Anshu Bathla original https://aws.amazon.com/blogs/security/can-i-do-that-with-policy-understanding-the-aws-service-authorization-reference/

Understanding what AWS Identity and Access Management (IAM) policies can control helps you build better security controls and avoid spending time on approaches that won’t work. You’ve likely encountered questions like:

  • Can I use AWS Organizations service control policies (SCPs) to prevent the creation of security groups that allow traffic from 0.0.0.0/0?
  • Can I block uploads unless objects are encrypted?
  • Can I prevent functions with more than 512 MB of memory allocated?

Some of these are possible with IAM policies. Others are not. The difference is determined by a fundamental principle of AWS authorization: Policies make decisions based on information available in the authorization context at the time of the API call.

In this blog post, you learn how to use the AWS Service Authorization Reference to determine what’s achievable with IAM policies, recognize scenarios that need alternative solutions, and build more effective security controls in your AWS environment.

Understanding AWS authorization context

When you make an AWS API request through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK, the specific AWS service (such as Amazon S3 or Amazon EC2) receiving the request assembles a request context containing information about that request. This context is used for policy evaluation decisions. Request context is structured using the Principal, Action, Resource, Condition (PARC) model, which has four key components.

  • Principal: Identifies the requester and their attributes (tags, session context)
  • Action: Specifies the AWS API operation being requested (for example, s3:PutObject, ec2:RunInstances)
  • Resource: Defines the target AWS resource using Amazon Resource Names (ARNs)
  • Condition: Provides additional context available at request time, such as IP address, time, encryption parameters, MFA status, and service-specific attributes

The following example shows the typical request context for an Amazon S3 object upload:

  • Principal: AIDA123456789EXAMPLE
  • Action: s3:PutObject
  • Resource: arn:aws:s3:::my-bucket/documents/samplereport.pdf
  • Condition:
    • aws:PrincipalTag/Department=Finance
    • aws:RequestedRegion=us-east-1
    • aws:SourceIp=x.x.x.x
    • aws:MultiFactorAuthPresent=true
    • s3:x-amz-server-side-encryption=AES256
    • s3:x-amz-storage-class=STANDARD_IA

IAM policies can evaluate request metadata like encryption method and storage class being specified. However, it cannot evaluate the actual file contents, object size, or specific data patterns. Policy evaluation occurs at the time of the request, using the information present in the authorization context.

An essential resource: The Service Authorization Reference

The Service Authorization Reference is the authoritative documentation for understanding what policies can control. For every AWS service, it documents:

  • Actions: Every controllable operation
  • Resources: Resource types that can be targeted
  • Condition keys: The exact context information available for policy decisions

Condition keys are broadly divided into two categories. Global condition keys, which can be used across AWS services, and service-specific condition keys, which are defined for use with an individual AWS service. Use the Service Authorization Reference to find the global-condition keys or service-specific condition keys for each AWS service.

How to use the Service Authorization Reference

Follow these steps to determine if your requirement can be controlled with IAM policies:

  1. Navigate to your service: Go to the page for the specific AWS service you’re working with, such as Actions, resources, and condition keys for Amazon S3.
  2. Find the action you want: Find the API operation you want to control. Be precise, different actions have different available condition keys.
  3. Examine available condition keys: The Condition keys column shows what context information AWS makes available for that action.
  4. Make your feasibility determination: If the information you need isn’t listed as a condition key, you will not be able to control it with IAM policies alone.

Let’s take an example from the Amazon Elastic Compute Cloud (Amazon EC2) ec2:RunInstances action to see what you can and can’t control. In the Service Authorization Reference under the Amazon EC2 section, examine the RunInstances action and check the Resource types column. The RunInstances action affects multiple resource types, each with its own set of condition keys.

For the instance* resource type:

  • ec2:InstanceType: Can restrict instance types
  • ec2:EbsOptimized: Can require EBS optimization
  • aws:RequestTag/: Can enforce tagging requirements

For the network-interface* resource type:

  • ec2:Subnet: Can control subnet placement
  • ec2:Vpc: Can limit to specific virtual private clouds (VPCs)
  • ec2:AssociatePublicIpAddress: Can control public IP assignment

Note: These are a few examples from the many condition keys available for each resource type under the RunInstances action. The Service Authorization Reference lists dozens of condition keys across resource types (instance, network interface, security group, subnet, volume, and so on) that RunInstances affects. Consult the complete reference to see the available options for your specific use case.

Access the Service Authorization Reference programmatically

Beyond the human-readable documentation, AWS provides the Service Authorization Reference in machine-readable JSON format to streamline automation of policy management workflows. Use this programmatic access to incorporate authorization metadata into your development and security workflows.
For detailed information about the JSON structure and field definitions, see the Simplified AWS service information for programmatic access.
Developers can use tools like the IAM MCP Server for AWS IAM operations. This server provides AI assistants with the ability to manage IAM users, roles, policies, and permissions while following security best practices.

Using IAM policies to control specific scenarios

The following examples show how you can use IAM policies to control specific scenarios.

Example 1: Enforce AES256 server-side encryption on S3 objects

In the Amazon S3 Service Authorization Reference, under s3:PutObject action, the s3:x-amz-server-side-encryption condition key is available in the authorization context, which can be used to control the server-side encryption of S3 objects with AES-256. Here is the required policy.

Policy 1: Deny Amazon S3 object upload if the encryption doesn’t use AES-256

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "DenyUnencryptedObjectUploads",
			"Effect": "Deny",
			"Action": "s3:PutObject",
			"Resource": "arn:aws:s3:::my-bucket/*",
			"Condition": {
				"StringNotEquals": {
					"s3:x-amz-server-side-encryption": "AES256"
				}
			}
		}
	]
}

Policy 1 is a resource-based policy that can be applied on an S3 bucket to restrict object uploads. It denies a PutObject request when the server-side encryption isn’t using the AES-256 encryption algorithm.

Example 2: Allow different instance types based on the user’s cost center tag.

When checking the Amazon EC2 Service Authorization Reference for ec2:RunInstances, the ec2:InstanceType condition key, which is resource specific, is available. To restrict instance types based on who is launching them (rather than just what is being launched), you can either combine this with a global condition key or attach different policies to different principals. By using aws:PrincipalTag/tag-key alongside ec2:InstanceType, you can identify the user’s cost center from their IAM identity tags and then apply different instance type restrictions accordingly. This allows a single policy to dynamically enforce different permissions based on the requester’s identity.

Policy 2: Restricting EC2 instance types by cost center

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "AllowDevInstanceTypes",
			"Effect": "Allow",
			"Action": "ec2:RunInstances",
			"Resource": "arn:aws:ec2:*:*:instance/*",
			"Condition": {
				"StringEquals": {
					"aws:PrincipalTag/CostCenter": "Development"
				},
				"StringLike": {
					"ec2:InstanceType": "t3.*"
				}
			}
		},
		{
			"Sid": "AllowProdInstanceTypes",
			"Effect": "Allow",
			"Action": "ec2:RunInstances",
			"Resource": "arn:aws:ec2:*:*:instance/*",
			"Condition": {
				"StringEquals": {
					"aws:PrincipalTag/CostCenter": "Production"
				},
				"StringLike": {
					"ec2:InstanceType": [
						"m5.*",
						"c5.*",
						"r5.*"
					]
				}
			}
		}
	]
}

This is an identity-based policy that you can attach to IAM users, groups, or roles to control EC2 instance launches based on cost allocation. In the first statement, aws:PrincipalTag, which is a global condition key (tags attached to the IAM user or role), is used to determine which instance types are allowed. Users tagged with CostCenter=Development can only launch cost-effective T3 instance types (t3.micro, t3.small, t3.medium, and so on)with the service specific key ec2:InstanceType.

In the second statement, users tagged with CostCenter=Production can launch more powerful instance types from the M5 (general purpose), C5 (compute optimized), and R5 (memory optimized) families. This approach lets organizations enforce cost controls and allocate resources based on workload requirements. Each cost center maintains flexibility for its specific needs.

Note: Additional resources are required in the IAM policy to successfully launch EC2 instances. For the complete list, see Launch Instances.

Example 3: Users can only access and update DynamoDB items where the partition key matches their username.

You have identified that GetItem, PutItem,and UpdateItem actions are required. Corresponding to these actions, you can use the condition key to expose partition key values in the authorization context as described in the Amazon DynamoDB Service Authorization Reference

Policy 3: DynamoDB fine-grained access control

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"dynamodb:GetItem",
				"dynamodb:PutItem",
				"dynamodb:UpdateItem"
			],
			"Resource": "arn:aws:dynamodb:us-east-1:111122223333:table/UserProfiles",
			"Condition": {
				"ForAllValues:StringEquals": {
					"dynamodb:LeadingKeys": ["${aws:username}"]
				}
			}
		}
	]
}

The policy allows users to perform read and write actions (GetItem, PutItem, and UpdateItem) on the UserProfiles table, but only for items where the partition key value equals their own username (using the ${aws:username} policy variable). For example, if user alice attempts to access an item with partition key bob, the request will be denied.

Scenarios that need more than policies alone

Some requirements can’t be met using IAM policies. Here are three common scenarios that aren’t achievable with IAM policies alone.

Scenario 1: Block users from creating security group rules that allow traffic from 0.0.0.0/0 on TCP port 22

Upon checking the Amazon EC2 Service Authorization Reference, you will find that the ec2:AuthorizeSecurityGroupIngress action is required in an IAM policy to add an inbound access rules to a security group.

To verify this in the Service Authorization Reference, navigate to the Amazon EC2 Service Authorization Reference and search for the AuthorizeSecurityGroupIngress action, which is the action that creates security group rules. After you locate this action, review the Condition keys column and look for condition keys related to CIDR blocks, IP ranges, ports, or protocols. Available condition keys for ec2:AuthorizeSecurityGroupIngress include:

Notice there are no condition keys for CIDR blocks (such as 0.0.0.0/0), port numbers (such as 22), or protocols (such as TCP). The authorization context doesn’t include information about the specific CIDR blocks, ports, or protocols being added to the security group rule, so IAM policies can’t control these attributes.

Solution
Take a reactive approach using the AWS Config managed rule INCOMING_SSH_DISABLED to detect overly permissive rules. You can also use a combination of Amazon EventBridge and Lambda to either send a notification to your security team for the non-compliant configuration or to restrict the security group through an automation. For more information, see How to Automatically Revert and Receive Notifications About Changes to Your Amazon VPC Security Groups.

Scenario 2: Prevent creation of Lambda functions with more than 512 MB of memory allocated

Following the same verification methodology described in Scenario 1, navigate to the AWS Lambda Service Authorization Reference and examine the CreateFunction action’s condition keys for the function* resource type.

Available condition keys for lambda:CreateFunction with the function* resource type include:

  • lambda:CodeSigningConfigArn: Filters access by the ARN of the code signing
  • configuration-lambda:Layer: Filters access by the ARN of a version of an AWS Lambda layer
  • lambda:VpcIds: Filters access by the ID of the VPC configured for the Lambda function

There is no condition key for memory allocation (MemorySize parameter), timeout settings, storage configuration (EphemeralStorage), or runtime selection. Because memory allocation isn’t exposed in the authorization context, IAM policies can’t restrict this parameter.

Solution

Key takeaways

Keep these principles in mind when working with IAM policies:

  • Policies control what’s in the authorization context, not all elements you see in API documentation
  • The Service Authorization Reference is authoritative; if something isn’t listed as a condition key, you can’t control it with policies
  • Different actions have different available contexts even within the same service
  • Alternative approaches exist. AWS Config, EventBridge, and service-specific controls can be used to achieve your goals when policies alone can’t
  • Layered security is essential; combine preventive, detective, and responsive controls to help ensure that your data is secure

Conclusion

In this post, you learned how to use the AWS Service Authorization Reference to determine what’s achievable with IAM policies and recognize scenarios that require alternative solutions. By understanding that policies can only make decisions based on information available in the authorization context, you can build more effective security controls and avoid spending time on approaches that won’t work.

The Service Authorization Reference is your authoritative source for understanding policy capabilities. When you need to implement a control, start there to see if the required condition keys exist. If they don’t, you will need to layer in detective or responsive controls using services like AWS Config, Amazon EventBridge, or AWS Lambda.

Remember that effective AWS security isn’t about finding one perfect control, it’s about combining preventive, detective, and responsive measures to create defense in depth. IAM policies are powerful tools for prevention and work as part of a comprehensive security strategy.

Next steps:

If you have feedback about this post, submit comments in the Comments section below.


Author

Anshu Bathla

Anshu is a Senior Lead Consultant – SRC at AWS, based in Gurugram, India. He works with customers across diverse verticals to help strengthen their security infrastructure and achieve their security goals. Outside of work, Anshu enjoys reading books and gardening at his home garden.

Author

Prafful Gupta

Prafful is an Associate Delivery Consultant at AWS, based in Gurugram, India. Having started his professional journey with Amazon, he specializes in DevOps and Generative AI solutions, helping customers navigate their cloud transformation journeys. Beyond work, he enjoys networking with fellow professionals and spending quality time with family.

Use AWS service reference information to automate policy management workflows

Post Syndicated from Ramesh Rajan original https://aws.amazon.com/blogs/security/use-aws-service-reference-information-to-automate-policy-management-workflows/

Amazon Web Services (AWS) provides service reference information in JSON format to help you automate policy management workflows. With the service reference information, you can access available actions across AWS services from machine-readable files. The service reference information helps to address a key customer need: keeping up with the ever-growing list of services and actions in AWS. As new services launch and existing services expand their capabilities, you can now conveniently identify and incorporate available actions, resources, and condition keys for each AWS service into your policy authoring and validation workflows. As your business expands and your AWS footprint grows, you might decide to automate your policy management workflows. With the service authorization reference, you can build custom tools to make it easier to evaluate and use new actions, resources, and condition keys that AWS services introduce.

Getting started with service reference information

The service reference information is static information about the actions, resources, and condition keys available for each service in AWS. To obtain the list of AWS services for which reference information is available, go to the following URL:
https://servicereference.us-east-1.amazonaws.com/v1/service-list.json

This URL endpoint provides a JSON file that contains an up-to-date catalog of AWS services with available reference information. By querying this endpoint, you can retrieve the most current list of services supported by the AWS Service Reference Information feature.

To retrieve the list of actions, resources, and condition keys for a specific AWS service, go to the following URL:
https://servicereference.us-east-1.amazonaws.com/v1/<service-name>/<service-name>.json

Replace <service-name> with the name of the desired AWS service (for example, “s3” for Amazon Simple Storage service (Amazon S3) or “ec2” for Amazon Elastic Compute Cloud (Amazon EC2)). This URL endpoint provides a JSON file that contains the comprehensive list of actions, resources, and condition keys that are available for that particular service.

The following example shows the format of the output from the service-list.json file, which contains the service names and URLs for each service’s reference information:

[ 
    {
"service": "s3", 
        "url": "https://servicereference.us-east-1.amazonaws.com/v1/s3/s3.json" 
    }, 
    {
"service": "dynamodb", 
        "url": "https://servicereference.us-east-1.amazonaws.com/v1/dynamodb/dynamodb.json" 
    }, 
    …
]

You can navigate to the service information page by using the url field to view the list of permissions for the service. You can also download the JSON file to use in your policy authoring workflows. For example, you can download the permissions for Amazon S3 by following this URL:
https://servicereference.us-east-1.amazonaws.com/v1/s3/s3.json

The following example shows a partial output of the permissions for Amazon S3. The AWS Identity and Access Management (IAM) actions are available in JSON format, and each action is its own JSON object. The Name field for those objects provides the name of the IAM action, the ActionConditionKeys field provides the available condition keys for this action, and the Resources field provides the available resources for this action.

{
  "Name" : "s3",
  "Actions" : [ {
    "Name" : "AbortMultipartUpload",
    "ActionConditionKeys" : [ "s3:AccessGrantsInstanceArn", "s3:AccessPointNetworkOrigin", "s3:DataAccessPointAccount", "s3:DataAccessPointArn", "s3:ResourceAccount", "s3:TlsVersion", "s3:authType", "s3:signatureAge", "s3:signatureversion", "s3:x-amz-content-sha256" ],
    "Resources" : [ {
      "Name" : "object"
    } ]
  }, {
    "Name" : "AssociateAccessGrantsIdentityCenter",
    "ActionConditionKeys" : [ "aws:ResourceTag/${TagKey}", "s3:ResourceAccount", "s3:TlsVersion", "s3:authType", "s3:signatureAge", "s3:signatureversion", "s3:x-amz-content-sha256" ],
    "Resources" : [ {
      "Name" : "accessgrantsinstance"
    } ],
    "Version": "v1.1" 
}

What can you build with the service reference information?

Let’s explore how you can make use of the service reference information through practical examples. To help you get started, here are two custom tools that use the service reference information. You can find these tools in our GitHub repository, ready for you to use and adapt to your specific needs. You can download the source code for these tools by visiting the following links:

SCP pre-processor

The SCP pre-processor provides a convenient way to write SCPs. You run the SCP pre-processor as a command-line tool. The tool takes a single, monolithic JSON file and runs a series of transformations and optimizations, then outputs a collection of valid service control policies that fit within policy size quotas. The tool uses AWS service reference information data in order to optimize lists of IAM actions.

Notification tool for new or removed IAM actions

You might find yourself needing to update various policies throughout your AWS environment when new IAM actions or services are released. You can use this tool to notify you when new services or new actions are added or removed. It works by downloading the service reference information and comparing it to the previous version of the file when the tool last ran. You can use these notifications to perform actions like automatically updating IAM policies when new actions are added or manually reviewing the notifications for new, sensitive actions.

Visit the source code repositories for the SCP pre-processor and the daily notification tool to learn more.

Conclusion

The AWS service reference information makes it easier for you to create automation for policy authoring and validation. By providing the AWS service actions reference in JSON format, this feature enables you to create custom tools for policy authoring and management.

We’re excited to know what kind of policy authoring tools you can think up.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Ramesh Rajan
Ramesh Rajan

Ramesh is a Senior Solutions Architect based out of San Francisco. He holds a Bachelor of Science in Applied Sciences and a Master’s in Cyber Security and Information Assurance. He specializes in cloud migration, cloud security, compliance, and risk management.
Matt Luttrell
Matt Luttrell

Matt is a Principal Solutions Architect on the AWS Identity Solutions team. When he’s not spending time chasing his kids around, he enjoys skiing, cycling, and the occasional video game.

Four ways to grant cross-account access in AWS

Post Syndicated from Anshu Bathla original https://aws.amazon.com/blogs/security/four-ways-to-grant-cross-account-access-in-aws/

As your Amazon Web Services (AWS) environment grows, you might develop a need to grant cross-account access to resources. This could be for various reasons, such as enabling centralized operations across multiple AWS accounts, sharing resources across teams or projects within your organization, or integrating with third-party services. However, granting cross-account access requires careful consideration of your security, availability, and manageability requirements.

In this blog post, we explore four different ways to grant cross-account access using resource-based policies. Each method has its own unique tradeoffs, and the best choice depends on your specific requirements and use case.

Evaluating different techniques for granting cross-account access

Cross-account access is granted by identity-based policies and resource-based policies in AWS Identity and Access Management (IAM). Identity-based policies attach to an IAM role, while resource-based polices attach to resources like Amazon Simple Storage Service (Amazon S3) buckets and AWS Key Management Service (AWS KMS) keys. Resource-based policies require you to specify one or more principals (IAM users or roles) that are allowed to access the resource.

Your choice of how to specify the principal in a resource-based policy impacts some aspects of both the confidentiality and the availability of your solution. Understanding this impact and making the right tradeoffs for your use case is the focus of this post.

An example scenario

Imagine that you have an S3 bucket in your AWS account (Account A) that needs to be accessed by different principals in another AWS account (Account B). For this scenario, we assume that the principals in Account B have the necessary access to S3 in their identity-based policies, and we will focus on authoring the resource-based policies in Account A. While the methods explained here use Amazon S3, the concepts discussed apply to all AWS services that support resource-based policies. In the following sections, we walk through four different ways to grant cross-account access in this scenario and discuss the tradeoffs of each.

Method 1: Grant access to a specific IAM role using the Principal element of the resource-based policy

In this example, you use an S3 bucket policy to grant access to a specific IAM role (RoleFromAccountB) in Account B by specifying the IAM role’s Amazon Resource Name (ARN) in the Principal element of the policy in Account A.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AllowRoleInThePrincipalElement",
      "Principal": {
        "AWS": "arn:aws:iam::111122223333:role/RoleFromAccountB"
      },
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::amzn-s3-demo-bucket-account-a/*"
    }
  ]
}

Using this bucket policy, if someone in Account B deletes or recreates the role (RoleFromAccountB), then that role can no longer access the amzn-s3-demo-bucket-account-a bucket, even if that role is recreated with the same name. The reason is that when you save this policy, the role ARN is mapped to the unique ID of the role, which looks something like this: AROADBQP57FF2AEXAMPLE. You will see a role identifier in the Principal element of your resource-based policies if you view them after you delete the role that they referenced.

This behavior is intentional. The resource-based policy only allows the specific instance of the role that you set as principal at the time of policy creation. This helps prevent unintended access to your resources if you delete a role, but forget to update your resource-based policy to remove that role. This behavior can also cause an availability risk because the role (RoleFromAccountB) will have a new unique ID when it is recreated and will no longer have access to the bucket. Roles can be recreated for a number of reasons, including accidentally when you use tools such as infrastructure as code.

You might consider choosing this method if:

  • You own the roles in both Account A and Account B and can control the creation and deletion of these roles.
  • You want your resource-based policy in Account A to stop granting access when the specified role (RoleFromAccountB) is deleted.
  • You prioritize granular access control over potential availability concerns if the role (RoleFromAccountB) is deleted.

Method 2: Grant access to an account using the Principal element of the resource-based policy

In this example, you grant access to a specific account in the Principal element of the resource-based policy. This resource-based policy of Account A allows any user or role from Account B that also has an identity-based policy that grants them access to read the objects.

Note: You can use either "Principal": {"AWS": "111122223333"} or "Principal": {"AWS": "arn:aws:iam::111122223333:root"} in the Principal element. They are equivalent, and the long-form ARN does not represent the root user.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AllowAccountInThePrincipalElement",
      "Principal": {
        "AWS": "111122223333"
      },
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::amzn-s3-demo-bucket-account-a/*"
    }
  ]
}

This resource-based policy helps avoid the potential availability issue discussed for Method 1. If a role in Account B that needs to have access to the bucket is recreated, it will still have access after the recreation of that role. This is because you don’t specify a role in the Principal element—instead, you specify an account. If you use Method 2, you must be comfortable delegating access control decisions to the owner of that account.

This approach explicitly delegates access control decisions to IAM in the other account (Account B). Principals in Account B have access to this bucket if allowed by their identity-based policies.

You might consider choosing this method if:

  • You need to grant access to many principals in Account B.
  • You want to delegate the access decision in the account where the principal exists (Account B).
  • You prioritize ease of management and availability over granular access control.

Method 3: Grant access to a specific IAM role using the aws:PrincipalArn condition

This method expands on Method 2 and adds a condition that grants access only to a specific IAM role. Similar to Method 2, you use the account number as the value of the Principal element, but also use the aws:PrincipalArn condition key to limit access to a specific principal in Account B.

The aws:PrincipalArn condition key is a global condition key that compares the ARN of the principal that made the request with the ARN that you specify in the policy. For IAM roles, the request context returns the ARN of the role, not the ARN of the user that assumed the role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AllowAccountInPrincipalAndRoleInPrincipalArn",
      "Principal": {
        "AWS": "111122223333"
      },
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::amzn-s3-demo-bucket-account-a/*",
      "Condition": {
        "ArnEquals": {
          "aws:PrincipalArn": "arn:aws:iam::111122223333:role/RoleFromAccountB"
        }
      }
    }
  ]
}

This policy comes with the same availability benefits as the policy in Method 2: access to this resource will survive role recreation. This is because the role is translated to its unique identifier only when it is used in the Principal element. It is not translated to a unique identifier when it is used in a condition. If the role (RoleFromAccountB) in Account B is recreated, accidentally or intentionally, the policy will continue to grant access because the role matches the role ARN specified in the condition key of the resource-based policy in Account A. As a result, Method 3 provides a balanced approach to availability and security.

You might consider choosing this method if:

  • You are comfortable that this policy will continue to grant access to the role specified in the aws:PrincipalArn condition key if that role (RoleFromAccountB) is recreated.
  • You don’t own the Account B you are granting access to and don’t control when that role may be recreated.
  • You want a balance of availability and confidentiality.

Method 4: Grant access to an entire AWS Organizations organization

This method is focused on a different use case and is not an alternative to the methods listed earlier. Use this method if you have a resource (an S3 bucket, in this example) that you want to share with your entire organization, but not share with anyone outside of it.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AllowAccessToAnEntireOrganization",
      "Principal": {
        "AWS": "*"
      },
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::amzn-s3-demo-bucket-account-a/*",
      "Condition": {
        "StringEquals": {
          "aws:PrincipalOrgId": "o-12345"
        },
        "StringNotEquals": {
          "aws:PrincipalAccount": "${aws:ResourceAccount}"
        }
      }
    }
  ]
}

There is no way to specify an organization by using the Principal element of a resource-based policy, so you must use the aws:PrincipalOrgId condition key to restrict access to a specific organization. In this policy, you specify a wildcard in the Principal element, which says that anyone can access the bucket. Then the condition reduces “anyone” to just those AWS account principals that belong to the specified organization and have an identity-based policy that allows them access.

You then add an additional conditional block that compares the aws:PrincipalAccount condition key to the aws:ResourceAccount condition key by using a policy variable. This extra conditional block is optional and excludes the account that owns the bucket (Account A) from the allow statement. The reason for using this extra conditional block is so that principals in Account A still require an allow statement in their identity-based policy to access this bucket. If you choose to exclude this aws:PrincipalAccount comparison, principals in Account A are granted access to the bucket without an explicit allow statement in their identity-based policy. Policy evaluation logic only requires either the identity-based policy or the resource-based policy (but not both) to allow a request when the principal and resource are in the same account.

You might consider choosing this method if:

  • You have a shared resource that should be accessible to your entire organization.

Conclusion

Choosing a method to grant cross-account access requires careful consideration of your requirements and use case. Each of the four methods discussed in this blog post has its own advantages and tradeoffs. By understanding these methods and their implications, you can decide on the most appropriate approach to grant cross-account access to your AWS resources. Remember to regularly review and audit your resource-based policies to verify that they align with your security and access requirements.

To learn how resource-based policies work with Amazon S3, see the blog post IAM Policies and Bucket Policies and ACLs! Oh My! Controlling Access to S3 Resources.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.
 

Anshu Bathla
Anshu Bathla

Anshu is a Lead Consultant – SRC at AWS, based in Gurugram, India. He works with customers across diverse verticals to help strengthen their security infrastructure and achieve their security goals. Outside of work, Anshu enjoys reading books and gardening at his home garden.
Jay Goradia
Jay Goradia

Jay is a Technical Account Manager (TAM) at AWS who works closely with enterprise customers to accelerate their cloud journey through strategic guidance and technical expertise. Using his security background, he helps organizations understand security best practices in AWS.

How to implement IAM policy checks with Visual Studio Code and IAM Access Analyzer

Post Syndicated from Anshu Bathla original https://aws.amazon.com/blogs/security/how-to-implement-iam-policy-checks-with-visual-studio-code-and-iam-access-analyzer/

In a previous blog post, we introduced the IAM Access Analyzer custom policy check feature, which allows you to validate your policies against custom rules. Now we’re taking a step further and bringing these policy checks directly into your development environment with the AWS Toolkit for Visual Studio Code (VS Code).

In this blog post, we show how you can integrate IAM Access Analyzer custom policy check capability into VS Code, so you can identify overly permissive IAM policies and fine-tune access controls early in the development process. This proactive approach to security and compliance helps to ensure that your IAM policies are validated before they are deployed, reducing the risk of introducing misconfigurations or granting unintended access. It also saves developer time by providing fast feedback to developers when they write a policy that does not meet organizational standards.

What is the problem?

Although security teams oversee an organization’s overall security posture, developers create applications that require specific permissions. To enable developers to work efficiently while maintaining high security standards, organizations often seek ways to safely delegate the authoring of AWS Identity and Access Management (IAM) policies to developers. Many AWS customers manually review developer-authored IAM policies before deploying them to production environments to help prevent granting excessive or unintended permissions. However, depending on the volume and complexity of policies, these manual reviews can be time-consuming, leading to development delays and potential bottlenecks in the deployment of applications and services. Organizations need to balance secure access management with the agility required for rapid application development and deployment.

How to use IAM Access Analyzer custom policy checks in VS Code

Custom policy checks are a feature in IAM Access Analyzer that are designed to help security teams proactively identify and analyze critical permissions within their IAM policies. In this section, we provide step-by-step instructions for using custom policy checks directly in VS Code.

Prerequisites

To complete the examples in our walkthrough, you first need to do the following:

  1. Install Python version 3.6 or later.
  2. Assuming you are already using the VS Code Integrated Development Environment (IDE), search for and install the AWS Toolkit extension.
  3. Configure your AWS role credentials to connect the toolkit to AWS.
  4. Install the IAM Policy Validator for AWS CloudFormation, available on GitHub. Alternatively, you can install the IAM Policy Validator for Terraform from GitHub if you are using Terraform as infrastructure-as-code in your organization.
  5. So that you can open IAM Access Analyzer policy checks in the VS Code editor, open the VS Code Command Palette by pressing Ctrl+Shift+P, search for IAM Policy Checks, and then choose AWS: Open IAM Policy Checks as shown in Figure 1.
    Figure 1: Search for the AWS: Open IAM Policy Checks option

    Figure 1: Search for the AWS: Open IAM Policy Checks option

By using the IAM policy checks option in VS Code, you can perform four types of checks:

We’ll walk through examples of each of these checks in the sections that follow.

Example 1: ValidatePolicy

In this example, we use the ValidatePolicy option provided by the IAM policy check plugin to validate IAM policies against IAM policy grammar and AWS best practices. When you run this check, you can view policy validation check findings that include security warnings, errors, general warnings, and suggestions for your policy. These actionable recommendations help you author policies that are aligned with AWS best practices.

To run the ValidatePolicy check

  1. Let’s use the following IAM policy for illustration purposes. You can see that resource * (a wildcard) is being used in the first statement, which indicates that the iam:PassRole action is allowed for all resources.
    {
        "Version": "2012-10-17",
        "Statement": [
          {
            "Effect": "Allow",
            "Action": "iam:PassRole",	
            "Resource": "*"
          },
          {
            "Effect": "Allow",
            "Action": ["s3:GetObject", "s3:PutObject"],
            "Resource": "arn:aws:s3:::amzn-s3-demo-bucket/*"
          }
        ]
      }
    

  2. In the VS Code editor, navigate to the IAM Policy Checks pane. Choose the document type JSON Policy Language and policy type Identity. Then choose Run Policy Validation.
    Figure 2: IAM Access Analyzer ValidatePolicy check results

    Figure 2: IAM Access Analyzer ValidatePolicy check results

    You can see that Access Analyzer has detected an issue, which is shown in the PROBLEMS pane.

    Figure 3: Problems pane with finding details for the ValidatePolicy check

    Figure 3: Problems pane with finding details for the ValidatePolicy check

    The security warning shown in Figure 3 states that the iam:PassRole action with a wildcard (*) in the resource can be overly permissive because it allows the ability to pass any IAM role in that account.

  3. Now, let’s modify the IAM policy by replacing the wildcard (*) with a specific role Amazon Resource Name (ARN).
    {
      "Version": "2012-10-17",
      "Statement": [
        {
          "Effect": "Allow",
          "Action": "iam:PassRole",
          "Resource": "arn:aws:iam::111122223333:role/sample_role"
        },
        {
          "Effect": "Allow",
          "Action": ["s3:GetObject", "s3:PutObject"],
          "Resource": "arn:aws:s3:::amzn-s3-demo-bucket/*"
        }
      ]
    }
    

  4. Verify the policy again by running the ValidatePolicy check to make sure that it doesn’t generate findings after you updated the IAM policy.
    Figure 4: Results of the ValidatePolicy check after IAM policy correction

    Figure 4: Results of the ValidatePolicy check after IAM policy correction

Example 2: CheckNoPublicAccess

With the CheckNoPublicAccess option, you can verify whether your resource policy grants public access for supported resource types.

To run the CheckNoPublicAccess check

  1. To test whether a policy does not allow public access, create a new bucket using a CloudFormation template and attach a resource policy that grants access to any principal to see the objects in this bucket.

    WARNING: This sample bucket policy should not be used in production. Using a wildcard in the principal element of a bucket policy would allow any IAM principal to view the contents of the bucket.

    Resources:
              MyBucket:
                Type: 'AWS::S3::Bucket'
                Properties:
                  BucketName: amzn-s3-demo-bucket
    
              MyBucketPolicy:
                Type: 'AWS::S3::BucketPolicy'
                Properties:
                  Bucket:
                    Ref: 'MyBucket'
                  PolicyDocument:
                    Version: '2012-10-17'
                    Statement:
                      - Effect: Allow
                        Principal: "*"
                        Action: 's3:GetObject'
                        Resource:
                          Fn::Join:
                            - ''
                            - - 'arn:aws:s3:::'
                              - Ref: 'MyBucket'
                              - '/*'
    

  2. Select the document type CloudFormation template and then choose Run Custom Policy Check to see whether this resource policy passes the CheckNoPublicAccess check.
    Figure 5: IAM Access Analyzer CheckNoPublicAccess check results

    Figure 5: IAM Access Analyzer CheckNoPublicAccess check results

    The policy check returns a failed result because this bucket does allow public access.

    Figure 6: Problems pane finding details for CheckNoPublicAccess check

    Figure 6: Problems pane finding details for CheckNoPublicAccess check

  3. Next, fix this policy to allow access from a role within the same account by restricting the policy to a specific role ARN.
    Resources:
              MyBucket:
                Type: 'AWS::S3::Bucket'
                Properties:
                  BucketName: amzn-s3-demo-bucket
    
              MyBucketPolicy:
                Type: 'AWS::S3::BucketPolicy'
                Properties:
                  Bucket:
                    Ref: 'MyBucket'
                  PolicyDocument:
                    Version: '2012-10-17'
                    Statement:
                      - Effect: Allow
                        Principal: 
                          "AWS": 'arn:aws:iam::111122223333:role/sample_role'
                        Action: 's3:GetObject'
                        Resource:
                          Fn::Join:
                            - ''
                            - - 'arn:aws:s3:::'
                              - Ref: 'MyBucket'
                              - '/*'
    

  4. Re-run the CheckNoPublicAccess check. The resource policy no longer grants public access and the status of the policy check is PASS.

Example 3: CheckAccessNotGranted

The CheckAccessNotGranted option allows you to check whether a policy allows access to a list of IAM actions and resource ARNs. You can use this check to give developers fast feedback that certain permissions or access to certain resources are not allowed.

To run the CheckAccessNotGranted check

  1. Identify sensitive actions and resources.

    In the VS Code editor, under Custom Policy Checks, choose the check type CheckAccessNotGranted. Using a comma-separated list, create a list of actions and resource ARNs that you don’t want to allow in your IAM policy. You can also create a JSON file with your actions and resources by using the syntax shown in Figure 7. For this example, set the s3:PutBucketPolicy and dynamodb:DeleteTable IAM actions to “not allowed” in the IAM policy.

    Figure 7: Configure the CheckAccessNotGranted check

    Figure 7: Configure the CheckAccessNotGranted check

  2. Create a sample CloudFormation template that contains an IAM policy attached to an IAM role, as follows. This policy grants access to some of the actions that you deemed sensitive in Figure 7.
    Resources:
      CreateTagsLambdaRole:
        Type: AWS::IAM::Role
        Properties:
          AssumeRolePolicyDocument:
            Version: '2012-10-17'
            Statement:
            - Effect: Allow
              Principal:
                Service: lambda.amazonaws.com
              Action: sts:AssumeRole
          Policies:
          - PolicyName: my-application-access
            PolicyDocument:
              Version: '2012-10-17'
              Statement:
              - Effect: Allow
                Action:
                - ec2:DescribeInstances
                Resource: "*"
              - Effect: Allow
                Action:
                - s3:GetObject
                - s3:PutBucketPolicy
                - dynamodb:DeleteTable
                Resource: "*"            
              
          RoleName: sample-role
    

  3. In the VS Code editor, choose Run Custom Policy Check to identify whether one of the sensitive actions or resources is allowed in the IAM policy. The policy check returns FAIL because the policy has the actions s3:PutBucketPolicy and dynamodb:DeleteTable, which you marked as actions that you don’t want developers to grant access to. Remove the restricted actions from the policy and run the check again to see a PASS result for the policy check.

Example 4: CheckNoNewAccess

The CheckNoNewAccess option is a custom policy check that verifies whether your policy grants new access compared to a reference policy.

You use a reference policy to check whether a candidate policy allows more access than the reference policy does. In other words, the check passes if the candidate policy is a subset of the reference policy. A reference policy typically starts by allowing all access. You then add a statement or statements that deny the access that you want the reference policy to check for. For more details and examples of reference policies, see the iam-access-analyzer-custom-policy-check-samples repository on GitHub.

The ability to use a reference policy provides you with the flexibility to look for almost anything in an IAM policy. This is useful when you have custom requirements for your organization that may not be met with some of the other custom policy checks.

To run the CheckNoNewAccess check

  1. Create a reference policy: In your project, create a new JSON policy document that will serve as your reference policy.

    The following reference policy checks that an IAM role trust policy only grants access to an allowlisted set of AWS services. This enables you to allow builders to create roles, but constrain the use of those roles to the set of AWS services specified.

    In this reference policy, only the specified AWS service principals ec2.amazonaws.com, lambda.amazonaws.com, and ecs-tasks.amazonaws.com are allowed to assume the role.

    {
      "Version": "2012-10-17",
      "Statement": [
        {
          "Sid": "AllowThisSetOfServicePrincipals",
          "Effect": "Allow",
          "Principal": {
            "Service": [
              "ec2.amazonaws.com",
              "lambda.amazonaws.com",
              "ecs-tasks.amazonaws.com"
            ]
          },
          "Action": "sts:AssumeRole"
        },
        {
          "Sid": "AllowOtherSTSActions",
          "Effect": "Allow",
          "Principal": "*",
          "NotAction": "sts:AssumeRole"
        }
      ]
    }
    

  2. Enter the reference policy in the VS Code editor. In the IAM Policy Checks pane, select the check type CheckNoNewAccess. Then set the reference policy type to Resource, because this is a trust policy that defines which principals can assume the role. In addition, provide the path of the reference policy that you created in Step 1. You can also directly enter the reference policy as a JSON policy document, as shown in Figure 8.
    Figure 8: Enter the reference policy for the CheckNoNewAccess check

    Figure 8: Enter the reference policy for the CheckNoNewAccess check

  3. Create a CloudFormation template, as follows. This template creates an IAM role that allows the AWS service principals lambda.amazonaws.com and glue.amazonaws.com to assume the sample-application-role IAM role.
    Resources:
      SampleApplicationRole:
        Type: AWS::IAM::Role
        Properties:
          AssumeRolePolicyDocument:
            Version: '2012-10-17'
            Statement:
            - Effect: Allow
              Principal:
                Service: 
                - lambda.amazonaws.com
                - glue.amazonaws.com
              Action: sts:AssumeRole
          Policies:
          - PolicyName: my-application-access
            PolicyDocument:
              Version: '2012-10-17'
              Statement:
              - Effect: Allow
                Action:
                - s3:GetObject
                Resource: "arn:aws:s3::111122223333:amzn-s3-demo-bucket/*"            
          RoleName: sample-application-role
    

  4. In the VS Code editor, choose Run Custom Policy Check to check your CloudFormation template against the reference policy you configured in Step 1. The check will return FAIL and you will see a security warning in the editor in the PROBLEMS pane.
    Figure 9: Problems pane finding details for the CheckNoNewAccess check

    Figure 9: Problems pane finding details for the CheckNoNewAccess check

    The issue is that glue.amazonaws.com was not listed as a service principal that was allowed to assume a role in your reference policy. You can remove glue.amazonaws.com from the CloudFormation template and re-run the check to receive a PASS result.

Conclusion

In this post, we explored how you can use the integration of VS Code with IAM Access Analyzer in your development workflow to make sure that your IAM policies align with best practices and adhere to your organization’s security requirements. The four critical checks provided by IAM Access Analyzer can be summarized as follows:

  • The ValidatePolicy check provides actionable recommendations that help you author policies that are aligned with AWS best practices.
  • The CheckNoPublicAccess check helps protect resources from being exposed publicly and mitigates the risk of unauthorized public access.
  • The CheckAccesNotGranted check looks for specific IAM actions and resource ARNs to help enforce access restrictions and help prevent unauthorized access to critical data or services.
  • The CheckNoNewAccess check validates that the permissions granted in your IAM policies remain within the intended scope, as defined by your organization’s requirements.

Install or update the AWS Toolkit for VS Code today, and make sure that you have the CloudFormation Policy Validator or Terraform Policy Validator, to take advantage of these features.

If you have feedback about this post, submit comments in the Comments section below.

Anshu Bathla

Anshu Bathla

Anshu is a Lead Consultant – SRC at AWS, based in Gurugram, India. He works with customers across diverse verticals to help strengthen their security infrastructure and achieve their security goals. Outside of work, Anshu enjoys reading books and gardening at his home garden.

Manoj Kumar

Manoj Kumar

Manoj is a Lead Consultant – SRC at AWS, based in Gurugram, India. He collaborates with diverse clients to design and implement comprehensive AWS Cloud security solutions. His expertise helps organizations fortify their cloud infrastructures, achieve compliance objectives, and provide robust data protection while using the advanced security features of AWS to support their business objectives.

Customize the scope of IAM Access Analyzer unused access analysis

Post Syndicated from Stéphanie Mbappe original https://aws.amazon.com/blogs/security/customize-the-scope-of-iam-access-analyzer-unused-access-analysis/

AWS Identity and Access Management Access Analyzer simplifies inspecting unused access to guide you towards least privilege. You can use unused access findings to identify over-permissive access granted to AWS Identity and Access Management (IAM) roles and users in your accounts or organization. From a delegated administrator account for IAM Access Analyzer, you can use the dashboard to review unused access findings across your organization and prioritize the accounts to inspect based on the volume and type of findings. The findings highlight unused roles, unused access keys for IAM users, and unused passwords for IAM users. For active IAM users and roles, the findings provide visibility into unused services and actions. Recently, IAM Access Analyzer launched new configuration capabilities that you can use to customize the analysis. You can select accounts, roles, and users to exclude, and focus on the areas that matter the most to you. You can use identifiers such as account ID or scale configuration using tags. By scoping the IAM Access Analyzer to monitor a subset of accounts and roles, you can reduce noise from unwanted findings. You can update the configuration when needed to change the scope of analysis. With this new offering, IAM Access Analyzer provides enhanced controls to help you tailor the analysis more closely to your organization’s security needs.

In this post, we walk you through an example scenario. Imagine that you’re a cloud administrator in a company that uses Amazon Web Services (AWS). You use AWS Organizations to organize your workload into several organizational units (OUs) and accounts. You have dedicated accounts for testing and experimenting with new AWS features called sandbox accounts across your organization. The sandbox accounts can be created by anyone in your company and are centrally recorded. You’re using tags on IAM resources and have followed AWS best practices and strategies when tagging your AWS resources. Tags are applied to the IAM roles created by your teams.

To make sure that your teams are following the principle of least privilege and are working with only the required permissions to access the AWS accounts, you use IAM Access Analyzer. You created an unused access analyzer at the organization level so it will monitor the AWS accounts in your organization. You noticed that you have multiple unused access findings. After analysis, your security team suggests the exclusion of some AWS accounts, IAM roles, and users so they can focus on the relevant findings. They want the sandbox accounts and the IAM roles they use for security purposes (such as auditing, incident response) to be excluded from the unused access analysis.

You can select accounts and roles to exclude when you create a new analyzer or update the analyzer later. In this post, we show you how to configure IAM Access Analyzer unused access finding to exclude specific accounts across your organization and specific principals (IAM roles and IAM users) once you have set up an analyzer. There is no additional pricing for using the prescriptive recommendations after you have enabled unused access findings.

Prerequisites

The following are the prerequisites to configure IAM Access Analyzer for unused access analysis:

  • An unused access analyzer created at the organization level
  • Administrative level access to the IAM Access Analyzer delegated administrator account
  • A list of account IDs that you want to exclude
  • IAM roles with tags

In the following sections, you will learn how to customize your IAM Access Analyzer to better suit your organization’s needs. This includes the following:

  1. Explore how to exclude specific AWS accounts from the analyzer’s unused access findings.
  2. See how to exclude tagged IAM roles from the analysis, allowing you to focus on the most relevant security insights and you see how to review exclusions on your analyzer to modify them as needed.
  3. By the end, you will have a tailored unused access analyzer that provides more meaningful and actionable results for your organization.

Exclude specific accounts across your organization

In this section, you will see how to update your existing unused access analyzer at the organization level through the AWS Management Console and AWS Command Line Interface (AWS CLI) to exclude specific AWS account IDs from its analysis.

If you don’t have an unused access analyzer in the organization, see this post for instructions on how to create one.

Use the console to update your unused access analyzer:

  1. Connect to your IAM Access Analyzer delegated administrator account (by default, your organization management account).
  2. Open the IAM Access Analyzer console in your management account. You will see the dashboard with your active finding by selecting the analyzer of your choice on the top right. In this example, the analyzer has 251 active findings.
    Figure 1: Unused access findings dashboard without exclusions

    Figure 1: Unused access findings dashboard without exclusions

  3. You can see the split of active findings per account. The example account has 57 active findings that you want to exclude from it.
    Figure 2: Unused access findings per account

    Figure 2: Unused access findings per account

  4. Select Analyzer settings under Access Analyzer in that navigation pane.
  5. The analyzer settings page presents the analyzers in your AWS Region and their status.
  6. Select your unused access analyzer in the list based on its name.
    Figure 3: Active access analyzers

    Figure 3: Active access analyzers

  7. On the Analyzer page, you can see the analyzer settings and a new tab called Exclusion. Because you have no excluded AWS accounts, the count of Excluded AWS accounts is 0 and there are no accounts displayed.
    Figure 4: Unused access analyzer exclusion tab

    Figure 4: Unused access analyzer exclusion tab

  8. Choose Manage in the Excluded AWS accounts section.
  9. Select Choose from organization and Hierarchy and choose Exclude next to the sandbox account that you want to exclude.
    Figure 5: Exclude sandbox account

    Figure 5: Exclude sandbox account

  10. After you select Exclude for the sandbox account, the account will be deselected and will appear in AWS accounts to exclude. The count of accounts to exclude has changed from 0 to 1. After you have finished, choose Save changes.
    Figure 6: Verify that the account is excluded and save changes

    Figure 6: Verify that the account is excluded and save changes

  11. The page will be automatically updated with your changes. You can then review the Excluded AWS accounts and verify that your excluded account is correctly configured.
    Figure 7: Analyzer configuration updated with excluded account

    Figure 7: Analyzer configuration updated with excluded account

  12. You can go back to the console dashboard and see the results. In this example, the exclusion of the sandbox account has caused the total number of active findings to go down from 251 to 194.
    Figure 8: Dashboard showing a reduction in active findings

    Figure 8: Dashboard showing a reduction in active findings

Use AWS CLI to update your unused access analyzer:

You can update your existing analyzer using the AWS CLI command aws accessanalyzer update-analyzer. Use the following command, replacing <YOUR-ANALYZER-NAME> with the name of your analyzer.

aws accessanalyzer update-analyzer 
--analyzer-name <YOUR-ANALYZER-NAME>
--configuration '{
  "unusedAccess": {
    "analysisRule": {
      "exclusions": [
        {
          "accountIds": [
            "222222222222"
          ]
        }
      ]
    }
  }
}'

You will obtain a result similar to the following:

{
    "revisionId": "<UNIQUE-REVISION-NUMBER>", 
    "configuration": {
        "unusedAccess": {
            "analysisRule": {
                "exclusions": [
                    {
                        "accountIds": [
                            "222222222222"
                        ]
                    }
                ]
            }, 
            "unusedAccessAge": 90
        }
    }

You have successfully excluded a sandbox account from the unused access analysis. Now you will exclude the IAM roles used by the security team to audit your accounts based on tags.

Excluding specific principals in your organization using tags

In this section, you will see how to update an existing unused access analyzer by excluding tagged IAM roles in your organization using the console and then AWS CLI.

Use the console to update your unused access analyzer:

  1. Open the IAM Access Analyzer console.
  2. Review the summary dashboard containing your unused findings. Choose Analyzer settings at the top of the screen.
    Figure 9: IAM Access Analyzer summary dashboard

    Figure 9: IAM Access Analyzer summary dashboard

  3. You will see a list of analyzers created in your account in that Region. Select the analyzer that you want to update.
  4. Review the analyzer page. On the Exclusion tab, you will see Exclude IAM users and roles with tags with a count of 0.
    Figure 10: Configure exclusion of IAM roles using tag

    Figure 10: Configure exclusion of IAM roles using tag

  5. Choose Manage in the Excluded IAM users and roles with tags section.
  6. Add the tags attached to the roles that you want to exclude from the analysis and choose Save changes.
    Figure 11: Add tag to exclude

    Figure 11: Add tag to exclude

  7. You can now see that Excluded IAM users and roles with tags now has a count of 1, and you can see the tags in the list.
    Figure 12: List of exclusion tags

    Figure 12: List of exclusion tags

Use AWS CLI to update your unused access analyzer:

You can also update your existing analyzer using the AWS CLI command aws accessanalyzer update-analyzer. Using the following command, replace <YOUR-ANALYZER-NAME> with the name of your analyzer.

aws accessanalyzer update-analyzer 
--analyzer-name <YOUR-ANALYZER-NAME> 
--configuration '{
  "unusedAccess": {
    "analysisRule": {
      "exclusions": [
        {
          "accountIds": [
            "222222222222"
          ]
        },
        {
          "resourceTags": [
            {
              "team": "security"
            }
          ]
        }
      ]
    }
  }
}'

A successful response will look like the following:

{
    "revisionId": "<UNIQUE-REVISION-NUMBER>", 
    "configuration": {
        "unusedAccess": {
            "analysisRule": {
                "exclusions": [
                    {
                        "accountIds": [
                            "222222222222"
                        ]
                    }, 
                    {
                        "resourceTags": [
                            {
                                "team": "security"
                            }
                        ]
                    }
                ]
            }, 
            "unusedAccessAge": 90
        }
    }
}

Review the exclusion on your analyzer

You can review, remove, or update the exclusions configured on your analyzer by using the console or AWS CLI. For example, as a security administrator managing multiple accounts, you might initially exclude IAM roles that have the tag security from analysis. However, you might need to review these exclusions if your policies change, requiring analysis of certain security roles or removing the exclusion entirely. By adjusting your exclusions, you can make sure that your analyzer’s results remain relevant to your organization’s needs and account structure.

Review the exclusion on unused access analyzer using the console:

In this section, review the tags that have been excluded from an analyzer.

  1. Open the IAM console.
  2. Select Access Analyzer, under Access reports, you will see a summary dashboard of findings from an analyzer.
    1. The Active findings section shows the number of active findings for unused roles, the number of active findings for unused credentials and the number of active findings for unused permissions.
    2. The Findings overview section includes a breakdown of the active findings.
    3. The Findings status section shows the status of findings (whether active, archived or resolved).
      Figure 13: Unused access analyzer dashboard

      Figure 13: Unused access analyzer dashboard

  3. Select the Analyzer settings at the top of the screen.
  4. Select the analyzer that you want to review to see the exclusion tags.
    Figure 14: Review unused access analyzer exclusions

    Figure 14: Review unused access analyzer exclusions

  1. After applying the tags, the updated dashboard is shown after the next scan.
    Figure 15: Dashboard showing reduction of findings after exclusions

    Figure 15: Dashboard showing reduction of findings after exclusions

Review the exclusion on an unused access analyzer using AWS CLI:

Using the name of your analyzer, you can run the command get-analyzer to see the configured exclusion. Using the following command, replace <YOUR-ANALYZER-NAME> with the name of your analyzer:

aws accessanalyzer get-analyzer --analyzer-name <YOUR-ANALYZER-NAME>

You will get a response similar to the following:

{
  "analyzer": {
    "status": "ACTIVE",
    "name": "<YOUR-ANALYZER-NAME>",
    "tags": {},
    "revisionId": "<UNIQUE-REVISION-NUMBER>",
    "arn": "arn:aws:access-analyzer:<REGION>:111111111111:analyzer/<YOUR-ANALYZER-NAME>",
    "configuration": {
      "unusedAccess": {
        "analysisRule": {
          "exclusions": [
            {
              "accountIds": [
                "222222222222"
              ]
            },
            {
              "resourceTags": [
                {
                  "team": "security"
                }
              ]
            }
          ]
        },
        "unusedAccessAge": 90
      }
    },
    "type": "ORGANIZATION_UNUSED_ACCESS",
    "createdAt": "2024-10-11T22:26:57Z"
  }
}

Conclusion

In this post, you learned how to tailor your unused access analyzer to your needs by excluding specific accounts and IAM roles. To exclude the accounts in your organization from being monitored by IAM Access Analyzer, you can use a list of account IDs or select them from a hierarchical view of your organization structure. You can exclude IAM roles and IAM users based on tags. By customizing the exclusion on the unused access analyzer, you saw that the number of active findings went down, helping you focus on the findings that matter most. With this new offering, IAM Access Analyzer provides enhanced controls to help you tailor the analysis more closely to your organization’s security needs.

If you have feedback about this post, submit comments in the Comments section below.

Stéphanie Mbappe

P. Stéphanie Mbappe

Stéphanie is a Security Consultant with Amazon Web Services. She delights in assisting her customers at any step of their security journey. Stéphanie enjoys learning, designing new solutions, and sharing her knowledge with others.

Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Reke Jarikre

Reke Jarikre

Reke is an Associate Security Consultant at Amazon Web Services. She is passionate about safeguarding client infrastructures and crafting robust security measures. Outside work, she enjoys exploring new technologies, public speaking and contributing to open-source projects.

Refine unused access using IAM Access Analyzer recommendations

Post Syndicated from Stéphanie Mbappe original https://aws.amazon.com/blogs/security/refine-unused-access-using-iam-access-analyzer-recommendations/

As a security team lead, your goal is to manage security for your organization at scale and ensure that your team follows AWS Identity and Access Management (IAM) security best practices, such as the principle of least privilege. As your developers build on AWS, you need visibility across your organization to make sure that teams are working with only the required privileges. Now, AWS Identity and Access Management Analyzer offers prescriptive recommendations with actionable guidance that you can share with your developers to quickly refine unused access.

In this post, we show you how to use IAM Access Analyzer recommendations to refine unused access. To do this, we start by focusing on the recommendations to refine unused permissions and show you how to generate the recommendations and the actions you can take. For example, we show you how to filter unused permissions findings, generate recommendations, and remediate issues. Now, with IAM Access Analyzer, you can include step-by-step recommendations to help developers refine unused permissions quickly.

Unused access recommendations

IAM Access Analyzer continuously analyzes your accounts to identify unused access and consolidates findings in a centralized dashboard. The dashboard helps review findings and prioritize accounts based on the volume of findings. The findings highlight unused IAM roles and unused access keys and passwords for IAM users. For active IAM roles and users, the findings provide visibility into unused services and actions. You can learn more about unused access analysis through the IAM Access Analyzer documentation.

For unused IAM roles, access keys, and passwords, IAM Access Analyzer provides quick links in the console to help you delete them. You can use the quick links to act on the recommendations or use export to share the details with the AWS account owner. For overly permissive IAM roles and users, IAM Access Analyzer provides policy recommendations with actionable steps that guide you to refine unused permissions. The recommended policies retain resource and condition context from existing policies, helping you update your policies iteratively.

Throughout this post, we use an IAM role in an AWS account and configure the permissions by doing the following:

  1. Attaching the AWS managed policy AmazonBedrockReadOnly.
  2. Attaching the AWS managed policy AmazonS3ReadOnlyAccess.
  3. Embedding an inline policy with the permissions described in the following code and named InlinePolicyListLambda.

Content of inline policy InlinePolicyListLambda:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "InlinePolicyLambda",
            "Effect": "Allow",
            "Action": [
                "lambda:ListFunctions",
                "lambda:ListLayers",
                "lambda:ListAliases",
                "lambda:ListFunctionUrlConfigs"
            ],
            "Resource": "*",
            "Condition": {
                "NotIpAddress": {
                    "aws:SourceIp": "1.100.150.200/32"
                }
            }
        }
    ]
}

We use an inline policy to demonstrate that IAM Access Analyzer unused access recommendations are applicable for that use case. The recommendations are also applicable when using AWS managed policies and customer managed policies.

In your AWS account, after you have configured an unused access analyzer, you can select an IAM role that you have used recently and see if there are unused access permissions findings and recommendations.

Prerequisites

Before you get started, you must create an unused access analyzer for your organization or account. Follow the instructions in IAM Access Analyzer simplifies inspection of unused access in your organization to create an unused access analyzer.

Generate recommendations for unused permissions

In this post we explore three options for generating recommendations for IAM Access Analyzer unused permissions findings: the console, AWS CLI, and AWS API.

Generate recommendations for unused permissions using the console

After you have created an unused access analyzer as described in the prerequisites, wait a few minutes to see the analysis results. Then use the AWS Management Console to view the proposed recommendations for the unused permissions.

To list unused permissions findings

  1. Go to the IAM console and under Access Analyzer, choose Unused access from the navigation pane.
  2. Search for active findings with the type Unused permissions in the search box.
    1. Select Active from the Status drop-down list.
    2. In the search box, select Findings type under Properties.
    3. Select Equals as Operators.
    4. Select Findings Type = Unused permissions.
    5. This list shows the active findings for IAM resources with unused permissions.

    Figure 1: Filter on unused permissions in the IAM console

    Figure 1: Filter on unused permissions in the IAM console

  3. Select a finding to learn more about the unused permissions granted to a given role or user.

To obtain recommendations for unused permissions

  1. On the findings detail page, you will see a list of the unused permissions under Unused permissions.
  2. Following that, there is a new section called Recommendations. The Recommendations section presents two steps to remediate the finding:
    1. Review the existing permissions on the resource.
    2. Create new policies with the suggested refined permissions and detach the existing policies.

    Figure 2: Recommendations section

    Figure 2: Recommendations section

  3. The generation of recommendations is on-demand and is done in the background when you’re using the console. The message Analysis in progress indicates that recommendations are being generated. The recommendations exclude the unused actions from the recommended policies.
  4. When an IAM principal, such as an IAM role or user, has multiple permissions policies attached, an analysis of unused permissions is made for each of permissions policies:
    • If no permissions have been used, the recommended action is to detach the existing permissions policy.
    • If some permissions have been used, only the used permissions are kept in the recommended policy, helping you apply the principle of least privilege.
  5. The recommendations are presented for each existing policy in the column Recommended policy. In this example, the existing policies are:
    • AmazonBedrockReadOnly
    • AmazonS3ReadOnlyAccess
    • InlinePolicyListLambda

    And the recommended policies are:

    • None
    • AmazonS3ReadOnlyAccess-recommended
    • InlinePolicyListLambda-recommended

    Figure 3: Recommended policies

    Figure 3: Recommended policies

  6. There is no recommended policy for AmazonBedrockReadOnly because the recommended action is to detach it. When hovering over None, the following message is displayed: There are no recommended policies to create for the existing permissions policy.
  7. AmazonS3ReadOnlyAccess and InlinePolicyListLambda and their associated recommended policy can be previewed by choosing Preview policy.

To preview a recommended policy

IAM Access Analyzer has proposed two recommended policies based on the unused actions.

  1. To preview each recommended policy, choose Preview policy for that policy to see a comparison between the existing and recommended permissions.
    1. Choose Preview policy for AmazonS3ReadOnlyAccess-recommended.
      1. The existing policy has been analyzed and the broad permissions—s3:Get* and s3:List*—have been scoped down to detailed permissions in the recommended policy.
      2. The permissions s3:Describe*, s3-object-lambda:Get*, and s3-object-lambda:List* can be removed because they weren’t used.

      Figure 4: Preview of the recommended policy for AmazonS3ReadOnlyAccess

      Figure 4: Preview of the recommended policy for AmazonS3ReadOnlyAccess

    2. Choose Preview policy for InlinePolicyListLambda-recommended to see a comparison between the existing inline policy InlinePolicyListLambda and its recommended version.
      1. The existing permissions, lambda:ListFunctions and lambda:ListLayers, are kept in the recommended policy, as well as the existing condition.
      2. The permissions in lambda:ListAliases and lambda:ListFunctionUrlConfigs can be removed because they weren’t used.
      Figure 5: Preview the recommended policy for the existing inline policy InlinePolicyListLambda

      Figure 5: Preview the recommended policy for the existing inline policy InlinePolicyListLambda

To download the recommended policies file

  1. Choose Download JSON to download the suggested recommendations locally.

    Figure 6: Download the recommended policies

    Figure 6: Download the recommended policies

  2. A .zip file that contains the recommended policies in JSON format will be downloaded.

    Figure 7: Downloaded recommended policies as JSON files

    Figure 7: Downloaded recommended policies as JSON files

  3. The content of the AmazonS3ReadOnlyAccess-recommended-1-2024-07-22T20/08/44.793Z.json file the same as the recommended policy shown in Figure 4.

Generate recommendations for unused permissions using AWS CLI

In this section, you will see how to generate recommendations for unused permissions using AWS Command Line Interface (AWS CLI).

To list unused permissions findings

  1. Use the following code to refine the results by filtering on the type UnusedPermission and selecting only the active findings. Copy the Amazon Resource Name (ARN) of your unused access analyzer and use it to replace the ARN in the following code:
    aws accessanalyzer list-findings-v2 \
      --analyzer-arn "arn:aws:access-analyzer:<region>:<123456789012>:analyzer/<analyzer_name>" \
      --region <region> \
      --filter '{"findingType": {"eq": ["UnusedPermission"]}, "status": {"eq": ["ACTIVE"]}}'

  2. You will obtain results similar to the following.
    {
        "findings": [
            {
                "analyzedAt": "2024-05-29T07:25:34+00:00",
                "createdAt": "2024-05-23T19:20:59+00:00",
                "id": "0fa3f5a1-bd92-4193-8ca4-aba12cd91370",
                "resource": "arn:aws:iam::123456789012:user/demoIAMUser",
                "resourceType": "AWS::IAM::User",
                "resourceOwnerAccount": "123456789012",
                "status": "ACTIVE",
                "updatedAt": "2024-05-29T07:25:35+00:00",
                "findingType": "UnusedPermission"
            },
            {
                "analyzedAt": "2024-05-29T07:25:34+00:00",
                "createdAt": "2024-05-23T19:20:59+00:00",
                "id": "1e952245-bcf3-48ad-a708-afa460df794b",
                "resource": "arn:aws:iam::123456789012:role/demoIAMRole",
                "resourceType": "AWS::IAM::Role",
                "resourceOwnerAccount": "123456789012",
                "status": "ACTIVE",
                "updatedAt": "2024-05-29T07:25:37+00:00",
                "findingType": "UnusedPermission"
            },
            ...
        ]
    }

To generate unused permissions finding recommendations

After you have a list of findings for unused permissions, you can generate finding recommendations.

  1. Run the following, replacing the analyzer ARN and the finding ID to generate the suggested recommendations.
    aws accessanalyzer generate-finding-recommendation \
      --analyzer-arn "arn:aws:access-analyzer:<region>:<123456789012>:analyzer/<analyzer_name>" \
      --region <region> \
      --id "ab123456-bcd0-78ab-a012-afa460df794b"

  2. You will get an empty response if your command ran successfully. The process is running in the background.

To obtain the generated recommendations

After the recommendations are generated, you need to make a separate API call to view the recommendations details.

  1. The following command returns the recommended remediation.
    aws accessanalyzer get-finding-recommendation \
      --analyzer-arn "arn:aws:access-analyzer:<region>:<123456789012>:analyzer/<analyzer_name>" \
      --region <region> \
      --id "ab123456-bcd0-78ab-a012-afa460df794b"

  2. This command provides the following results. For more information about the meaning and structure of the recommendations, see Anatomy of a recommendation later in this post.

    Note: The recommendations consider AWS managed policies, customer managed policies, and inline policies. The IAM conditions in the initial policy are maintained in the recommendations if the actions they’re related to are used.

    The remediations suggested are to do the following:

    1. Detach AmazonBedrockReadOnly policy because it is unused: DETACH_POLICY
    2. Create a new recommended policy with scoped down permissions from the managed policy AmazonS3ReadOnlyAccess: CREATE_POLICY
    3. Detach AmazonS3ReadOnlyAccess: DETACH_POLICY
    4. Embed a new recommended policy with scoped down permissions from the inline policy: CREATE_POLICY
    5. Delete the inline policy.
    {
        "recommendedSteps": [
            {
                "unusedPermissionsRecommendedStep": {
                    "policyUpdatedAt": "2023-12-06T15:48:19+00:00",
                    "recommendedAction": "DETACH_POLICY",
                    "existingPolicyId": "arn:aws:iam::aws:policy/AmazonBedrockReadOnly"
                }
            },
            {
                "unusedPermissionsRecommendedStep": {
                    "policyUpdatedAt": "2023-08-10T21:31:39+00:00",
                    "recommendedAction": "CREATE_POLICY",
                    "recommendedPolicy": "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"Allow\",\"Action\":[\"s3:GetBucketObjectLockConfiguration\",\"s3:GetBucketOwnershipControls\",\"s3:GetBucketTagging\",\"s3:GetBucketVersioning\",\"s3:GetJobTagging\",\"s3:GetObject\",\"s3:GetObjectAcl\",\"s3:GetObjectLegalHold\",\"s3:GetObjectRetention\",\"s3:GetObjectTagging\",\"s3:GetObjectTorrent\",\"s3:GetObjectVersion*\",\"s3:GetStorage*\",\"s3:ListAllMyBuckets\",\"s3:ListBucket\",\"s3:ListBucketVersions\",\"s3:ListMultipartUploadParts\",\"s3:ListStorageLensGroups\",\"s3:ListTagsForResource\"],\"Resource\":\"*\"}]}",
                    "existingPolicyId": "arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess"
                }
            },
            {
                "unusedPermissionsRecommendedStep": {
                    "policyUpdatedAt": "2023-08-10T21:31:39+00:00",
                    "recommendedAction": "DETACH_POLICY",
                    "existingPolicyId": "arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess"
                }
            },
            {
                "unusedPermissionsRecommendedStep": {
                    "recommendedAction": "CREATE_POLICY",
                    "recommendedPolicy": "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Sid\":\"InlinePolicyLambda\",\"Effect\":\"Allow\",\"Action\":[\"lambda:ListFunctions\",\"lambda:ListLayers\"],\"Resource\":\"*\",\"Condition\":{\"NotIpAddress\":{\"aws:SourceIp\":\"1.100.150.200/32\"}}}]}",
                    "existingPolicyId": "InlinePolicyListLambda"
                }
            },
            {
                "unusedPermissionsRecommendedStep": {
                    "recommendedAction": "DETACH_POLICY",
                    "existingPolicyId": "InlinePolicyListLambda"
                }
            }
        ],
        "status": "SUCCEEDED",
        "error": null,
        "completedAt": "2024-07-22T20:40:58.413698+00:00",
        "recommendationType": "UNUSED_PERMISSION_RECOMMENDATION",
        "resourceArn": "arn:aws:iam::123456789012:role/IAMRole_IA2_Blog_EC2Role",
        "startedAt": "2024-07-22T20:40:54+00:00"
    }

Generate recommendations for unused permissions using EventBridge and AWS API

We have described how to use AWS CLI and the console to find unused permissions findings and to generate recommendations.

In this section, we show you how to use an Amazon EventBridge rule to find the active unused permissions findings from IAM Access Analyzer. Then we show you how to generate recommendations using two IAM Access Analyzer APIs to generate the finding recommendations and get the finding recommendations.

To create an EventBridge rule to detect unused permissions findings

Create an EventBridge rule to detect new unused permissions findings from IAM Access Analyzer.

  1. Go to the Amazon EventBridge console.
  2. Choose Rules, and then choose Create rule.
  3. Enter a name for your rule. Leave the Event bus value as the default.
  4. Under Rule type, select Rule with an event pattern.
  5. In the Event Source section, select AWS events or EventBridge partner events.
  6. For Creation method, select Use pattern form.
  7. Under Event pattern:
    1. For Event source, select AWS services.
    2. For AWS service, select Access Analyzer.
    3. For Event type, select Unused Access Finding for IAM entities.

    Note: There is no event for generated recommendations, only for unused access findings.

    Figure 8: Listing unused permissions by filtering events using an EventBridge rule

    Figure 8: Listing unused permissions by filtering events using an EventBridge rule

  8. Configure the Event pattern by changing the default values to the following:
    1. resources: Enter the ARN of your unused access analyzer.
    2. status: ACTIVE indicates that you are only looking for active findings.
    3. findingType: UnusedPermission.
  9. You can select a target Amazon Simple Notification Service (Amazon SNS) to be notified of new active findings for a specific analyzer for unused permissions.

To generate recommendations for unused permissions using the IAM Access Analyzer API

The findings are generated on-demand. For that purpose, IAM Access Analyzer API GenerateFindingRecommendation can be called with two parameters: the ARN of the analyzer and the finding ID.

  1. You can use AWS Software Development Kit (SDK) for Python(boto3) for the API call.
  2. Run the call as follows:
    ia2_client = boto3.client('accessanalyzer')
    response = ia2_client.generate_finding_recommendation(
        analyzerArn=analyzer,
        id=findingId
        )

To obtain the finding recommendations

  1. After the recommendations are generated, they can be obtained by calling the API GetFindingRecommendation with the same parameters: the ARN of the analyzer and the finding ID.
  2. Use AWS SDK for Python (boto3) for the API call as follows:
    ia2_client = boto3.client('accessanalyzer')
    response = ia2_client.get_finding_recommendation(
        analyzerArn=analyzer,
        id=findingId
    )

Remediate based on the generated recommendations

The recommendations are generated as actionable guidance that you can follow. They propose new IAM policies that exclude the unused actions, helping you rightsize your permissions.

Anatomy of a recommendation

The recommendations are usually presented in the following way:

  • Date and time: startedAt, completedAt. Respectively when the API call was made and when the analysis was completed and the results were provided.
  • Resource ARN: The ARN of the resource being analyzed.
  • Recommended steps: The recommended steps, such as creating a new policy based on the actions used and detaching the existing policy.
  • Recommendation type: UNUSED_PERMISSION_RECOMMENDATION.
  • Status: The status of retrieving the finding recommendation. The status values include SUCCEEDED, FAILED, and IN_PROGRESS.

For more information about the structure of recommendations, see the output section of get-finding-recommendation.

Recommended policy review

You must review the recommended policy. The recommended actions depend on the original policy. The original policy will be one of the following:

  • An AWS managed policy: You need to create a new IAM policy using recommendedPolicy. Attach this newly created policy to your IAM role. Then detach the former policy.
  • A customer managed policy or an inline policy: Review the policy, verify its scope, consider how often it’s attached to other principals (customer managed policy only), and when you are confident to proceed, use the recommended policy to create a new policy and detach the former policy.

Use cases to consider when reviewing recommendations

During your review process, keep in mind that the unused actions are determined based on the time defined in your tracking period. The following are some use cases you might have where a necessary role or action might be identified as unused (this is not an exhaustive list of use cases). It’s important to review the recommendations based on your business needs. You can also archive some findings related to the use cases such as the ones that follow:

  • Backup activities: If your tracking period is 28 days and you have a specific role for your backup activities running at the end of each month, you might discover that after 29 days some of the permissions for that backup role are identified as unused.
  • IAM permissions associated to an infrastructure as code deployment pipeline: You should also consider the permissions associated to specific IAM roles such an IAM for infrastructure as code (IaC) deployment pipeline. Your pipeline can be used to deploy Amazon Simple Storage Service (Amazon S3) buckets based on your internal guidelines. After deployment is complete, the pipeline permissions can become unused after your tracking period, but removing those unused permissions can prevent you from updating your S3 buckets configuration or from deleting it.
  • IAM roles associated with disaster recovery activities: While it’s recommended to have a disaster recovery plan, the IAM roles used to perform those activities might be flagged by IAM Access Analyzer for having unused permissions or being unused roles.

To apply the suggested recommendations

Of the three original policies attached to IAMRole_IA2_Blog_EC2Role, AmazonBedrockReadOnly can be detached and AmazonS3ReadOnlyAccess and InlinePolicyListLambda can be refined.

  1. Detach AmazonBedrockReadOnly

    No permissions are used in this policy, and the recommended action is to detach it from your IAM role. To detach it, you can use the IAM console, the AWS CLI, or the AWS API.

  2. Create a new policy called AmazonS3ReadOnlyAccess-recommended and detach AmazonS3ReadOnlyAccess.

    The unused access analyzer has identified unused permissions in the managed policy AmazonS3ReadOnlyAccess and proposed a new policy AmazonS3ReadOnlyAccess-recommended that contains only the used actions. This is a step towards least privilege because the unused actions can be removed by using the recommended policy.

    1. Create a new IAM policy named AmazonS3ReadOnlyAccess-recommended that contains only the following recommended policy or one based on the downloaded JSON file.
      {
        "Version": "2012-10-17",
        "Statement": [
          {
            "Effect": "Allow",
            "Action": [
              "s3:getbuckettagging",
              "s3:getjobtagging",
              "s3:getobject",
              "s3:getobjectacl",
              "s3:getobjectlegalhold",
              "s3:getobjectretention",
              "s3:getobjecttagging",
              "s3:getobjecttorrent",
              "s3:getobjectversion",
              "s3:getobjectversionacl",
              "s3:getobjectversionattributes",
              "s3:getobjectversionforreplication",
              "s3:getobjectversiontagging",
              "s3:getobjectversiontorrent",
              "s3:getstoragelensconfigurationtagging",
              "s3:getstoragelensgroup",
              "s3:listbucket",
              "s3:listbucketversions",
              "s3:listmultipartuploadparts",
              "s3:liststoragelensgroups",
              "s3:listtagsforresource"
            ],
            "Resource": "*"
          }
        ]
      }

    2. Detach the managed policy AmazonS3ReadOnlyAccess.
  3. Embed a new inline policy InlinePolicyListLambda-recommended and delete InlinePolicyListLambda. This inline policy lists AWS Lambda aliases, functions, layers, and function URLs only when coming from a specific source IP address.
    1. Embed the recommended inline policy.

      The recommended policy follows. You can embed an inline policy for the IAM role using the console, AWS CLI, or the AWS API PutRolePolicy.

      {
        "Version": "2012-10-17",
        "Statement": [
          {
            "Sid": "InlinePolicyLambda",
            "Effect": "Allow",
            "Action": [
              "lambda:ListFunctions",
              "lambda:ListLayers"
            ],
            "Resource": "*",
            "Condition": {
              "NotIpAddress": {
                "aws:SourceIp": "1.100.150.200/32"
              }
            }
          }
        ]
      }

    2. Delete the inline policy.
  4. After updating the policies based on the Recommended policy proposed, the finding Status will change from Active to Resolved.

    Figure 9: The finding is resolved

    Figure 9: The finding is resolved

Pricing

There is no additional pricing for using the prescriptive recommendations after you have enabled unused access findings.

Conclusion

As a developer writing policies, you can use the actionable guidance provided in recommendations to continually rightsize your policies to include only the roles and actions you need. You can export the recommendations through the console or set up automated workflows to notify your developers about new IAM Access Analyzer findings.

This new IAM Access Analyzer unused access recommendations feature streamlines the process towards least privilege by selecting the permissions that are used and retaining the resource and condition context from existing policies. It saves an impressive amount of time by the actions used by your principals and guiding you to refine them.

By using the IAM Access Analyzer findings and access recommendations, you can quickly see how to refine the permissions granted. We have shown in this blog post how to generate prescriptive recommendations with actionable guidance for unused permissions using AWS CLI, API calls, and the console.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

P. Stéphanie Mbappe

P. Stéphanie Mbappe
Stéphanie is a Security Consultant with Amazon Web Services. She delights in assisting her customers at any step of their security journey. Stéphanie enjoys learning, designing new solutions, and sharing her knowledge with others.

Mathangi Ramesh

Mathangi Ramesh
Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Refine permissions for externally accessible roles using IAM Access Analyzer and IAM action last accessed

Post Syndicated from Nini Ren original https://aws.amazon.com/blogs/security/refine-permissions-for-externally-accessible-roles-using-iam-access-analyzer-and-iam-action-last-accessed/

When you build on Amazon Web Services (AWS) across accounts, you might use an AWS Identity and Access Management (IAM) role to allow an authenticated identity from outside your account—such as an IAM entity or a user from an external identity provider—to access the resources in your account. IAM roles have two types of policies attached to them: a trust policy that allows access to an external entity, and a permissions policy that defines what actions the role can take. This blog post focuses on how to use AWS Identity and Access Management Access Analyzer cross-account access findings and IAM action last accessed information to refine the permissions policies of your IAM roles that have a trust policy.

IAM Access Analyzer helps you set, verify, and refine permissions. To learn more about how IAM Access Analyzer guides you toward least-privilege permissions, visit Using AWS IAM Access Analyzer. Action last accessed information helps you identify unused permissions and refine the access of your IAM roles to only the actions they use. IAM now provides action last accessed information for more than 140 services such as Amazon Kinesis Data Streams and Data Firehose, Amazon DynamoDB, and Amazon Simple Queue Service (Amazon SQS).

This blog post walks you through how to use IAM Access Analyzer and action last accessed to refine the required permissions for your IAM roles that have a trust policy, which allows entities outside of your account to assume a role and access your resources.

Use IAM roles to grant access to an external entity

You can create an IAM role that grants permissions for an entity outside your account to access the resources in your account. For example, if you’re an application developer, you might grant cross-account access to your AWS resources by using a role and attaching a trust policy to the role.

To allow an external entity access to your resources by using a role, you first create a role with a role trust policy to grant access to entities outside your account, and then grant permissions that specify which actions the role can take. The external entities can then assume the role in your account and access your resources based on the permissions you granted to the role. See Cross-account access using roles for more information.

You should restrict the access of roles that grant access outside of your account to just the permissions required to perform a specific task.

Use IAM Access Analyzer cross-account access findings to identify roles that grant access to external entities

When you use role trust policies to grant account access to entities outside your account, those entities can access and take the allowed actions on your resources. IAM Access Analyzer continuously monitors your account to identify the resources in your account that can be accessed from outside your account and helps you verify whether the access permissions meet your intent. For the example in this post, if you were to add a new trust policy to your
ApplicationRole
to grant permissions to an external account to access an application in your account, IAM Access Analyzer would let you know that ApplicationRole is accessible by entities from outside your account.

Use IAM action last accessed information to identify and remove unused permissions

After you’ve identified the IAM roles that grant access to entities outside your account, review what those roles can do and remove unused permissions. You can use action last accessed to show you the latest timestamp when your IAM role used an action, analyze its access permissions, and remove unused permissions.

Refine permissions for externally accessible roles by using IAM Access Analyzer cross-account access findings and action last accessed information

This example demonstrates how you can combine the information from IAM Access Analyzer cross-account access findings and IAM action last accessed information to identify roles that can be assumed from outside your account, review unused and unnecessary actions, and reduce the permissions available to external roles.

To view action last accessed information in the IAM console

  1. Open the AWS Management Console and go to the IAM console, and then select Access analyzer in the navigation pane.
  2. If you’ve already created an analyzer, go to Step 3. Otherwise, follow Identify Unintended Resource Access with IAM Access Analyzer to create an analyzer.
  3. Review your findings on the IAM Access Analyzer tab.
  4. Under Active findings, for Filter active findings, enter AWS::IAM::Role. The list of Active findings shows you the roles that can be accessed by entities outside your account.
  5. Figure 1: Findings filtered by resource types

    Figure 1: Findings filtered by resource types

  6. Under the Finding ID column, select a finding for a role (for example, ApplicationRole) that you want to review.
  7. A new page for the Finding ID will appear. Choose the resource ARN link in the Resource field under the Details section.
  8. Figure 2: Findings page

    Figure 2: Findings page

  9. A new page for the role will appear. Select the Access Advisor tab to review the last accessed information of your services for this role. This tab displays the AWS services to which the role has permissions. Action last accessed reports the actions listed in the IAM action last accessed information services and actions. The tracking period for services is the last 400 days—fewer if your AWS Region began tracking within the last 400 days. Learn more about Where AWS tracks last accessed information.
  10. Figure 3: Last accessed information of allowed services

    Figure 3: Last accessed information of allowed services

  11. In this exercise, we will use DynamoDB as an example. Under Allowed services, for Search, enter Amazon DynamoDB and under the Service column, choose Amazon DynamoDB. This will take you to a new section titled Allowed management actions for Amazon DynamoDB, which displays the action last accessed information of your role for DynamoDB. The Action column displays the action, the Last Accessed column displays the timestamp of when access was last attempted, and the Region accessed column displays in which region access was last attempted.
  12. The Action column on the resulting Allowed management actions for Amazon DynamoDB section includes the actions to which the role has permissions, when the role last accessed each action, and the Region accessed. You can sort the actions by choosing the arrow next to Last accessed.
  13. Figure 4: Action last accessed information for Amazon DynamoDB

    Figure 4: Action last accessed information for Amazon DynamoDB

  14. Because you want to remove unused permissions, filter for all unused actions for the role by selecting Services not accessed from the Last accessed dropdown list. This will show you the actions that haven’t been accessed during the tracking period.
  15. Figure 5: Action last accessed information ordered by not accessed

    Figure 5: Action last accessed information ordered by not accessed

  16. To return to the service view, choose Back to Allowed services and then select the Permissions tab. Select the plus sign to the left of DynamoDBAccess to see the JSON of the customer managed policy.
  17. Figure 6: The JSON code of the customer managed policy

    Figure 6: The JSON code of the customer managed policy

  18. Choose Edit and remove dynamodb:* and replace it with just the actions that have been used recently such as: DescribeTable and DescribeKinesisStreamingDestination. Not all actions are reported by action last accessed. Review the list of actions that action last accessed information reports and when action last accessed started tracking the action for the service in an AWS Region.
  19. Choose Next and then Save changes. Return to the Access Advisor tab to confirm that all the retained permissions have been used recently.

Conclusion

In this post, you learned how to use IAM Access Analyzer and action last accessed information to identify and refine permissions for externally accessible roles in your journey toward least privilege. You first used IAM Access Analyzer cross-account access findings to identify IAM roles that can be accessed from outside your account. You then used IAM action last accessed information to review the permissions those roles are using and to remove unused permissions.

For more information about IAM Access Analyzer cross-account findings, see Findings for public and cross-account access. For more information about action last accessed information, see Things to know about last accessed information and the IAM action last accessed information services and actions.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS re:Post or contact AWS Support.

Nini Ren

Nini Ren

Nini is a product manager for AWS Identity and Access Management and AWS Resource Access Manager. He enjoys working with customers to develop solutions that create value for their businesses. Nini holds an MBA from The Wharton School, a Master of computer and information technology from the University of Pennsylvania, and an AB in chemistry and physics from Harvard College.

Mathangi Ramesh

Mathangi Ramesh

Mathangi is a product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Two real-life examples of why limiting permissions works: Lessons from AWS CIRT

Post Syndicated from Richard Billington original https://aws.amazon.com/blogs/security/two-real-life-examples-of-why-limiting-permissions-works-lessons-from-aws-cirt/

Welcome to another blog post from the AWS Customer Incident Response Team (CIRT)! For this post, we’re looking at two events that the team was involved in from the viewpoint of a regularly discussed but sometimes misunderstood subject, least privilege. Specifically, we consider the idea that the benefit of reducing permissions in real-life use cases does not always require using the absolute minimum set of privileges. Instead, you need to weigh the cost and effort of creating and maintaining privileges against the risk reduction that is achieved, to make sure that your permissions are appropriate for your needs.

To quote VP and Distinguished Engineer at Amazon Security, Eric Brandwine, “Least privilege equals maximum effort.” This is the idea that creating and maintaining the smallest possible set of privileges needed to perform a given task will require the largest amount of effort, especially as customer needs and service features change over time. However, the correlation between effort and permission reduction is not linear. So, the question you should be asking is: How do you balance the effort of privilege reduction with the benefits it provides?

Unfortunately, this is not an easy question to answer. You need to consider the likelihood of an unwanted issue happening, the impact if that issue did happen, and the cost and effort to prevent (or detect and recover from) that issue. You also need to factor requirements such as your business goals and regulatory requirements into your decision process. Of course, you won’t need to consider just one potential issue, but many. Often it can be useful to start with a rough set of permissions and refine them down as you develop your knowledge of what security level is required. You can also use service control policies (SCPs) to provide a set of permission guardrails if you’re using AWS Organizations. In this post, we tell two real-world stories where limiting AWS Identity and Access Management (IAM) permissions worked by limiting the impact of a security event, but where the permission set did not involve maximum effort.

Story 1: On the hunt for credentials

In this AWS CIRT story, we see how a threat actor was unable to achieve their goal because the access they obtained — a database administrator’s — did not have the IAM permissions they were after.

Background and AWS CIRT engagement

A customer came to us after they discovered unauthorized activity in their on-premises systems and in some of their AWS accounts. They had incident response capability and were looking for an additional set of hands with AWS knowledge to help them with their investigation. This helped to free up the customer’s staff to focus on the on-premises analysis.

Before our engagement, the customer had already performed initial containment activities. This included rotating credentials, revoking temporary credentials, and isolating impacted systems. They also had a good idea of which federated user accounts had been accessed by the threat actor.

The key part of every AWS CIRT engagement is the customer’s ask. Everything our team does falls on the customer side of the AWS Shared Responsibility Model, so we want to make sure that we are aligned to the customer’s desired outcome. The ask was clear—review the potential unauthorized federated users’ access, and investigate the unwanted AWS actions that were taken by those users during the known timeframe. To get a better idea of what was “unwanted,” we talked to the customer to understand at a high level what a typical day would entail for these users, to get some context around what sort of actions would be expected. The users were primarily focused on working with Amazon Relational Database Service (RDS).

Analysis and findings

For this part of the story, we’ll focus on a single federated user whose apparent actions we investigated, because the other federated users had not been impersonated by the threat actor in a meaningful way. We knew the approximate start and end dates to focus on and had discovered that the threat actor had performed a number of unwanted actions.

After reviewing the actions, it was clear that the threat actor had performed a console sign-in on three separate occasions within a 2-hour window. Each time, the threat actor attempted to perform a subset of the following actions:

Note: This list includes only the actions that are displayed as readOnly = false in AWS CloudTrail, because these actions are often (but not always) the more impactful ones, with the potential to change the AWS environment.

This is the point where the benefit of permission restriction became clear. As soon as this list was compiled, we noticed that two fields were present for all of the actions listed:

"errorCode": "Client.UnauthorizedOperation",
"errorMessage": "You are not authorized to perform this operation. [rest of message]"

As this reveals, every single non-readOnly action that was attempted by the threat actor was denied because the federated user account did not have the required IAM permissions.

Customer communication and result

After we confirmed the findings, we had a call with the customer to discuss the results. As you can imagine, they were happy that the results showed no material impact to their data, and said no further investigation or actions were required at that time.

What were the IAM permissions the federated user had, which prevented the set of actions the threat actor attempted?

The answer did not actually involve the absolute minimal set of permissions required by the user to do their job. It’s simply that the federated user had a role that didn’t have an Allow statement for the IAM actions the threat actor tried — their job did not require them. Without an explicit Allow statement, the IAM actions attempted were denied because IAM policies are Deny by default. In this instance, simply not having the desired IAM permissions meant that the threat actor wasn’t able to achieve their goal, and stopped using the access. We’ll never know what their goal actually was, but trying to create access keys, passwords, and update policies means that a fair guess would be that they were attempting to create another way to access that AWS account.

Story 2: More instances for crypto mining

In this AWS CIRT story, we see how a threat actor’s inability to create additional Amazon Elastic Compute Cloud (Amazon EC2) instances resulted in the threat actor leaving without achieving their goal.

Background and AWS CIRT engagement

Our second story involves a customer who had an AWS account they were using to test some new third-party software that uses Amazon Elastic Container Service (Amazon ECS). This customer had Amazon GuardDuty turned on, and found that they were getting GuardDuty alerts for CryptoCurrency:EC2/BitcoinTool related findings.

Because this account was new and currently only used for testing their software, the customer saw that the detection was related to the Amazon ECS cluster and decided to delete all the resources in the account and rebuild. Not too long after doing this, they received a similar GuardDuty alert for the new Amazon ECS cluster they had set up. The second finding resulted in the customer’s security team and AWS being brought in to try to understand what was causing this. The customer’s security team was focused on reviewing the tasks that were being run on the cluster, while AWS CIRT reviewed the AWS account actions and provided insight about the GuardDuty finding and what could have caused it.

Analysis and findings

Working with the customer, it wasn’t long before we discovered that the 3rd party Amazon ECS task definition that the customer was using, was unintentionally exposing a web interface to the internet. That interface allowed unauthenticated users to run commands on the system. This explained why the same alert was also received shortly after the new install had been completed.

This is where the story takes a turn for the better. The AWS CIRT analysis of the AWS CloudTrail logs found that there were a number of attempts to use the credentials of the Task IAM role that was associated with the Amazon ECS task. The majority of actions were attempting to launch multiple Amazon EC2 instances via RunInstances calls. Every one of these actions, along with the other actions attempted, failed with either a Client.UnauthorizedOperation or an AccessDenied error message.

Next, we worked with the customer to understand the permissions provided by the Task IAM role. Once again, the permissions could have been limited more tightly. However, this time the goal of the threat actor — running a number of Amazon EC2 instances (most likely for surreptitious crypto mining) — did not align with the policy given to the role:

{
    "Version": "2012-10-17",
    "Statement": [
        {
          "Effect": "Allow",
          "Action": "s3:*",
          "Resource": "*"
        }
    ]
}

AWS CIRT recommended creating policies to restrict the allowed actions further, providing specific resources where possible, and potentially also adding in some conditions to limit other aspects of the access (such as the two Condition keys launched recently to limit where Amazon EC2 instance credentials can be used from). However, simply having the policy limit access to Amazon Simple Storage Service (Amazon S3) meant that the threat actor decided to leave with just the one Amazon ECS task running crypto mining rather than a larger number of Amazon EC2 instances.

Customer communication and result

After reporting these findings to the customer, there were two clear next steps: First, remove the now unwanted and untrusted Amazon ECS resource from their AWS account. Second, review and re-architect the Amazon ECS task so that access to the web interface was only provided to appropriate users. As part of that re-architecting, an Amazon S3 policy similar to “Allows read and write access to objects in an S3 bucket” was recommended. This separates Amazon S3 bucket actions from Amazon S3 object actions. When applications have a need to read and write objects in Amazon S3, they don’t normally have a need to create or delete entire buckets (or versioning on those buckets).

Some tools to help

We’ve just looked at how limiting privileges helped during two different security events. Now, let’s consider what can help you decide how to reduce your IAM permissions to an appropriate level. There are a number of resources that talk about different approaches:

The first approach is to use Access Analyzer to help generate IAM policies based on access activity from log data. This can then be refined further with the addition of Condition elements as desired. We already have a couple of blog posts about that exact topic:

The second approach is similar, and that is to reduce policy scope based on the last-accessed information:

The third approach is a manual method of creating and refining policies to reduce the amount of work required. For this, you can begin with an appropriate AWS managed IAM policy or an AWS provided example policy as a starting point. Following this, you can add or remove Actions, Resources, and Conditions — using wildcards as desired — to balance your effort and permission reduction.

An example of balancing effort and permission is in the IAM tutorial Create and attach your first customer managed policy. In it, the authors create a policy that uses iam:Get* and iam:List:* in the Actions section. Although not all iam:Get* and iam:List:* Actions may be required, this is a good way to group similar Actions together while minimizing Actions that allow unwanted access — for example, iam:Create* or iam:Delete*. Another example of this balancing was mentioned earlier relating to Amazon S3, allowing access to create, delete, and read objects, but not to change the configuration of the bucket those objects are in.

In addition to limiting permissions, we also recommend that you set up appropriate detection and response capability. This will enable you to know when an issue has occurred and provide the tools to contain and recover from the issue. Further details about performing incident response in an AWS account can be found in the AWS Security Incident Response Guide.

There are also two services that were used to help in the stories we presented here — Amazon GuardDuty and AWS CloudTrail. GuardDuty is a threat detection service that continuously monitors your AWS accounts and workloads for malicious activity. It’s a great way to monitor for unwanted activity within your AWS accounts. CloudTrail records account activity across your AWS infrastructure and provides the logs that were used for the analysis that AWS CIRT performed for both these stories. Making sure that both of these are set up correctly is a great first step towards improving your threat detection and incident response capability in AWS.

Conclusion

In this post, we looked at two examples where limiting privilege provided positive results during a security event. In the second case, the policy used should probably have restricted permissions further, but even as it stood, it was an effective preventative control in stopping the unauthorized user from achieving their assumed goal.

Hopefully these stories will provide new insight into the way your organization thinks about setting permissions, while taking into account the effort of creating the permissions. These stories are a good example of how starting a journey towards least privilege can help stop unauthorized users. Neither of the scenarios had policies that were least privilege, but the policies were restrictive enough that the unauthorized users were prevented from achieving their goals this time, resulting in minimal impact to the customers. However in both cases AWS CIRT recommended further reducing the scope of the IAM policies being used.

Finally, we provided a few ways to go about reducing permissions—first, by using tools to assist with policy creation, and second, by editing existing policies so they better fit your specific needs. You can get started by checking your existing policies against what Access Analyzer would recommend, by looking for and removing overly permissive wildcard characters (*) in some of your existing IAM policies, or by implementing and refining your SCPs.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Richard Billington

Richard Billington

Richard is the Incident Response Watch Lead for the Asia-Pacific region of the AWS Customer Incident Response Team (a team that supports AWS Customers during active security events). He also helps customers prepare for security events using event simulations. Outside of work, he loves wildlife photography and Dr Pepper (which is hard to find in meaningful quantities within Australia).

Use IAM Access Analyzer policy generation to grant fine-grained permissions for your AWS CloudFormation service roles

Post Syndicated from Joel Knight original https://aws.amazon.com/blogs/security/use-iam-access-analyzer-policy-generation-to-grant-fine-grained-permissions-for-your-aws-cloudformation-service-roles/

AWS Identity and Access Management (IAM) Access Analyzer provides tools to simplify permissions management by making it simpler for you to set, verify, and refine permissions. One such tool is IAM Access Analyzer policy generation, which creates fine-grained policies based on your AWS CloudTrail access activity—for example, the actions you use with Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and Amazon Simple Storage Service (Amazon S3). AWS has expanded policy generation capabilities to support the identification of actions used from over 140 services. New additions include services such as AWS CloudFormation, Amazon DynamoDB, and Amazon Simple Queue Service (Amazon SQS). When you request a policy, IAM Access Analyzer generates a policy by analyzing your CloudTrail logs to identify actions used from this group of over 140 services. The generated policy makes it efficient to grant only the required permissions for your workloads. For other services, Access Analyzer helps you by identifying the services used and guides you to add the necessary actions.

In this post, we will show how you can use Access Analyzer to generate an IAM permissions policy that restricts CloudFormation permissions to only those actions that are necessary to deploy a given template, in order to follow the principle of least privilege.

Permissions for AWS CloudFormation

AWS CloudFormation lets you create a collection of related AWS and third-party resources and provision them in a consistent and repeatable fashion. A common access management pattern is to grant developers permission to use CloudFormation to provision resources in the production environment and limit their ability to do so directly. This directs developers to make infrastructure changes in production through CloudFormation, using infrastructure-as-code patterns to manage the changes.

CloudFormation can create, update, and delete resources on the developer’s behalf by assuming an IAM role that has sufficient permissions. Cloud administrators often grant this IAM role broad permissions–in excess of what’s necessary to just create, update, and delete the resources from the developer’s template–because it’s not clear what the minimum permissions are for the template. As a result, the developer could use CloudFormation to create or modify resources outside of what’s required for their workload.

The best practice for CloudFormation is to acquire permissions by using the credentials from an IAM role you pass to CloudFormation. When you attach a least-privilege permissions policy to the role, the actions CloudFormation is allowed to perform can be scoped to only those that are necessary to manage the resources in the template. In this way, you can avoid anti-patterns such as assigning the AdministratorAccess or PowerUserAccess policies—both of which grant excessive permissions—to the role.

The following section will describe how to set up your account and grant these permissions.

Prepare your development account

Within your development account, you will configure the same method for deploying infrastructure as you use in production: passing a role to CloudFormation when you launch a stack. First, you will verify that you have the necessary permissions, and then you will create the role and the role’s permissions policy.

Get permissions to use CloudFormation and IAM Access Analyzer

You will need the following minimal permissions in your development account:

  • Permission to use CloudFormation, in particular to create, update, and delete stacks
  • Permission to pass an IAM role to CloudFormation
  • Permission to create IAM roles and policies
  • Permission to use Access Analyzer, specifically the GetGeneratedPolicy, ListPolicyGenerations, and StartPolicyGeneration actions

The following IAM permissions policy can be used to grant your identity these permissions.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "DeveloperPermissions”,
            "Effect": "Allow",
            "Action": [
                "access-analyzer:GetGeneratedPolicy",
                "access-analyzer:ListPolicyGenerations",
                "access-analyzer:StartPolicyGeneration",
                "cloudformation:*",
                "iam:AttachRolePolicy",
                "iam:CreatePolicy",
                "iam:CreatePolicyVersion",
                "iam:CreateRole",
                "iam:DeletePolicyVersion",
                "iam:DeleteRolePolicy",
                "iam:DetachRolePolicy",
                "iam:GetPolicy",
                "iam:GetPolicyVersion",
                "iam:GetRole",
                "iam:GetRolePolicy",
                "iam:ListPolicies",
                "iam:ListPolicyTags",
                "iam:ListPolicyVersions",
                "iam:ListRolePolicies",
                "iam:ListRoleTags",
                "iam:ListRoles",
                "iam:PutRolePolicy",
                "iam:UpdateAssumeRolePolicy"
            ],
            "Resource": "*"
        },
        {
            "Sid": "AllowPassCloudFormationRole”,
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ]
            "Resource": "*",
            "Condition": {
                "StringEquals": {
                    "iam:PassedToService": "cloudformation.amazonaws.com"
                }
            }
        }
    ]
}

Note: If your identity already has these permissions through existing permissions policies, there is no need to apply the preceding policy to your identity.

Create a role for CloudFormation

Creating a service role for CloudFormation in the development account makes it less challenging to generate the least-privilege policy, because it becomes simpler to identify the actions CloudFormation is taking as it creates and deletes resources defined in the template. By identifying the actions CloudFormation has taken, you can create a permissions policy to match.

To create an IAM role in your development account for CloudFormation

  1. Open the IAM console and choose Roles, then choose Create role.
  2. For the trusted entity, choose AWS service. From the list of services, choose CloudFormation.
  3. Choose Next: Permissions.
  4. Select one or more permissions policies that align with the types of resources your stack will create. For example, if your stack creates a Lambda function and an IAM role, choose the AWSLambda_FullAccess and IAMFullAccess policies.

    Note: Because you have not yet created the least-privilege permissions policy, the role is granted broader permissions than required. You will use this role to launch your stack and evaluate the resulting actions that CloudFormation takes, in order to build a lower-privilege policy.

  5. Choose Next: Tags to proceed.
  6. Enter one or more optional tags, and then choose Next: Review.
  7. Enter a name for the role, such as CloudFormationDevExecRole.
  8. Choose Create role.

Create and destroy the stack

To have CloudFormation exercise the actions required by the stack, you will need to create and destroy the stack.

To create and destroy the stack

  1. Navigate to CloudFormation in the console, expand the menu in the left-hand pane, and choose Stacks.
  2. On the Stacks page, choose Create Stack, and then choose With new resources.
  3. Choose Template is ready, choose Upload a template file, and then select the file for your template. Choose Next.
  4. Enter a Stack name, and then choose Next.
  5. For IAM execution role name, select the name of the role you created in the previous section (CloudFormationDevExecRole). Choose Next.
  6. Review the stack configuration. If present, select the check box(es) in the Capabilities section, and then choose Create stack.
  7. Wait for the stack to reach the CREATE_COMPLETE state before continuing.
  8. From the list of stacks, select the stack you just created, choose Delete, and then choose Delete stack.
  9. Wait until the stack reaches the DELETE_COMPLETE state (at which time it will also disappear from the list of active stacks).

Note: It’s recommended that you also modify the CloudFormation template and update the stack to initiate updates to the deployed resources. This will allow Access Analyzer to capture actions that update the stack’s resources, in addition to create and delete actions. You should also review the API documentation for the resources that are being used in your stack and identify any additional actions that may be required.

Now that the development environment is ready, you can create the least-privilege permissions policy for the CloudFormation role.

Use Access Analyzer to generate a fine-grained identity policy

Access Analyzer reviews the access history in AWS CloudTrail to identify the actions an IAM role has used. Because CloudTrail delivers logs within an average of about 15 minutes of an API call, you should wait at least that long after you delete the stack before you attempt to generate the policy, in order to properly capture all of the actions.

Note: CloudTrail must be enabled in your AWS account in order for policy generation to work. To learn how create a CloudTrail trail, see Creating a trail for your AWS account in the AWS CloudTrail User Guide.

To generate a permissions policy by using Access Analyzer

  1. Open the IAM console and choose Roles. In the search box, enter CloudFormationDevExecRole and select the role name in the list.
  2. On the Permissions tab, scroll down and choose Generate policy based on CloudTrail events to expand this section. Choose Generate policy.
  3. Select the time period of the CloudTrail logs you want analyzed.
  4. Select the AWS Region where you created and deleted the stack, and then select the CloudTrail trail name in the drop-down list.
  5. If this is your first time generating a policy, choose Create and use a new service role to have an IAM role automatically created for you. You can view the permissions policy the role will receive by choosing View permission details. Otherwise, choose Use an existing service role and select a role in the drop-down list.

    The policy generation options are shown in Figure 1.

    Figure 1: Policy generation options

    Figure 1: Policy generation options

  6. Choose Generate policy.

You will be redirected back to the page that shows the CloudFormationDevExecRole role. The Status in the Generate policy based on CloudTrail events section will show In progress. Wait for the policy to be generated, at which time the status will change to Success.

Review the generated policy

You must review and save the generated policy before it can be applied to the role.

To review the generated policy

  1. While you are still viewing the CloudFormationDevExecRole role in the IAM console, under Generate policy based on CloudTrail events, choose View generated policy.
  2. The Generated policy page will open. The Actions included in the generated policy section will show a list of services and one or more actions that were found in the CloudTrail log. Review the list for omissions. Refer to the IAM documentation for a list of AWS services for which an action-level policy can be generated. An example list of services and actions for a CloudFormation template that creates a Lambda function is shown in Figure 2.
    Figure 2: Actions included in the generated policy

    Figure 2: Actions included in the generated policy

  3. Use the drop-down menus in the Add actions for services used section to add any necessary additional actions to the policy for the services that were identified by using CloudTrail. This might be needed if an action isn’t recorded in CloudTrail or if action-level information isn’t supported for a service.

    Note: The iam:PassRole action will not show up in CloudTrail and should be added manually if your CloudFormation template assigns an IAM role to a service (for example, when creating a Lambda function). A good rule of thumb is: If you see iam:CreateRole in the actions, you likely need to also allow iam:PassRole. An example of this is shown in Figure 3.

    Figure 3: Adding PassRole as an IAM action

    Figure 3: Adding PassRole as an IAM action

  4. When you’ve finished adding additional actions, choose Next.

Generated policies contain placeholders that need to be filled in with resource names, AWS Region names, and other variable data. The actual values for these placeholders should be determined based on the content of your CloudFormation template and the Region or Regions you plan to deploy the template to.

To replace placeholders with real values

  • In the generated policy, identify each of the Resource properties that use placeholders in the value, such as ${RoleNameWithPath} or ${Region}. Use your knowledge of the resources that your CloudFormation template creates to properly fill these in with real values.
    • ${RoleNameWithPath} is an example of a placeholder that reflects the name of a resource from your CloudFormation template. Replace the placeholder with the actual name of the resource.
    • ${Region} is an example of a placeholder that reflects where the resource is being deployed, which in this case is the AWS Region. Replace this with either the Region name (for example, us-east-1), or a wildcard character (*), depending on whether you want to restrict the policy to a specific Region or to all Regions, respectively.

For example, a statement from the policy generated earlier is shown following.

{
    "Effect": "Allow",
    "Action": [
        "lambda:CreateFunction",
        "lambda:DeleteFunction",
        "lambda:GetFunction",
        "lambda:GetFunctionCodeSigningConfig"
    ],
    "Resource": "arn:aws:lambda:${Region}:${Account}:function:${FunctionName}"
},

After substituting real values for the placeholders in Resource, it looks like the following.

{
    "Effect": "Allow",
    "Action": [
        "lambda:CreateFunction",
        "lambda:DeleteFunction",
        "lambda:GetFunction",
        "lambda:GetFunctionCodeSigningConfig"
    ],
    "Resource": "arn:aws:lambda:*:123456789012:function:MyLambdaFunction"
},

This statement allows the Lambda actions to be performed on a function named MyLambdaFunction in AWS account 123456789012 in any Region (*). Substitute the correct values for Region, Account, and FunctionName in your policy.

The IAM policy editor window will automatically identify security or other issues in the generated policy. Review and remediate the issues identified in the Security, Errors, Warnings, and Suggestions tabs across the bottom of the window.

To review and remediate policy issues

  1. Use the Errors tab at the bottom of the IAM policy editor window (powered by IAM Access Analyzer policy validation) to help identify any placeholders that still need to be replaced. Access Analyzer policy validation reviews the policy and provides findings that include security warnings, errors, general warnings, and suggestions for your policy. To find more information about the different checks, see Access Analyzer policy validation. An example of policy errors caused by placeholders still being present in the policy is shown in Figure 4.
    Figure 4: Errors identified in the generated policy

    Figure 4: Errors identified in the generated policy

  2. Use the Security tab at the bottom of the editor window to review any security warnings, such as passing a wildcard (*) resource with the iam:PassRole permission. Choose the Learn more link beside each warning for information about remediation. An example of a security warning related to PassRole is shown in Figure 5.
    Figure 5: Security warnings identified in the generated policy

    Figure 5: Security warnings identified in the generated policy

Remediate the PassRole With Star In Resource warning by modifying Resource in the iam:PassRole statement to list the Amazon Resource Name (ARN) of any roles that CloudFormation needs to pass to other services. Additionally, add a condition to restrict which service the role can be passed to. For example, to allow passing a role named MyLambdaRole to the Lambda service, the statement would look like the following.

        {
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ],
            "Resource": [
                "arn:aws:iam::123456789012:role/MyLambdaRole"
            ],
            "Condition": {
                "StringEquals": {
                    "iam:PassedToService": [
                        "lambda.amazonaws.com"
                    ]
                }
            }
        }

The generated policy can now be saved as an IAM policy.

To save the generated policy

  1. In the IAM policy editor window, choose Next.
  2. Enter a name for the policy and an optional description.
  3. Review the Summary section with the list of permissions in the policy.
  4. Enter optional tags in the Tags section.
  5. Choose Create and attach policy.

Test this policy by replacing the existing role policy with this newly generated policy. Then create and destroy the stack again so that the necessary permissions are granted. If the stack fails during creation or deletion, follow the steps to generate the policy again and make sure that the correct values are being used for any iam:PassRole statements.

Deploy the CloudFormation role and policy

Now that you have the least-privilege policy created, you can give this policy to the cloud administrator so that they can deploy the policy and CloudFormation service role into production.

To create a CloudFormation template that the cloud administrator can use

  1. Open the IAM console, choose Policies, and then use the search box to search for the policy you created. Select the policy name in the list.
  2. On the Permissions tab, make sure that the {}JSON button is activated. Select the policy document by highlighting from line 1 all the way to the last line in the policy, as shown in Figure 6.
    Figure 6: Highlighting the generated policy

    Figure 6: Highlighting the generated policy

  3. With the policy still highlighted, use your keyboard to copy the policy into the clipboard (Ctrl-C on Linux or Windows, Option-C on macOS).
  4. Paste the permissions policy JSON object into the following CloudFormation template, replacing the <POLICY-JSON-GOES-HERE> marker. Be sure to indent the left-most curly braces of the JSON object so that they are to the right of the PolicyDocument keyword.
    AWSTemplateFormatVersion: '2010-09-09'
    
    Parameters:
      PolicyName:
        Type: String
        Description: The name of the IAM policy that will be created
    
      RoleName:
        Type: String
        Description: The name of the IAM role that will be created
    
    Resources:
      CfnPolicy:
        Type: AWS::IAM::ManagedPolicy
        Properties:
          ManagedPolicyName: !Ref PolicyName
          Path: /
          PolicyDocument: >
            <POLICY-JSON-GOES-HERE>
    
      CfnRole:
        Type: AWS::IAM::Role
        Properties:
          RoleName: !Ref RoleName
          AssumeRolePolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Action:
                - sts:AssumeRole
                Effect: Allow
                Principal:
                  Service:
                    - cloudformation.amazonaws.com
          ManagedPolicyArns:
            - !Ref CfnPolicy
          Path: /

    For example, after pasting the policy, the CfnPolicy resource in the template will look like the following.

    CfnPolicy:
        Type: AWS::IAM::ManagedPolicy
        Properties:
          ManagedPolicyName: !Ref PolicyName
          Path: /
          PolicyDocument: >
            {
                "Version": "2012-10-17",
                "Statement": [
                    {
                        "Effect": "Allow",
                        "Action": "ec2:DescribeNetworkInterfaces",
                        "Resource": [
                            "*"
                        ]
                    },
                    {
                        "Effect": "Allow",
                        "Action": [
                            "iam:AttachRolePolicy",
                            "iam:CreateRole",
                            "iam:DeleteRole",
                            "iam:DetachRolePolicy",
                            "iam:GetRole"
                        ],
                        "Resource": [
                            "arn:aws:iam::123456789012:role/MyLambdaRole"
                        ]
                    },
                    {
                        "Effect": "Allow",
                        "Action": [
                            "lambda:CreateFunction",
                            "lambda:DeleteFunction",
                            "lambda:GetFunction",
                            "lambda:GetFunctionCodeSigningConfig"
                        ],
                        "Resource": [
                            "arn:aws:lambda:*:123456789012:function:MyLambdaFunction"
                        ]
                    },
                    {
                        "Effect": "Allow",
                        "Action": [
                            "iam:PassRole"
                        ],
                        "Resource": [
                            "arn:aws:iam::123456789012:role/MyLambdaRole"
                        ],
                        "Condition": {
                            "StringEquals": {
                                "iam:PassedToService": [
                                    "lambda.amazonaws.com"
                                ]
                            }
                        }
                    }
                ]
            }

  5. Save the CloudFormation template and share it with the cloud administrator. They can use this template to create an IAM role and permissions policy that CloudFormation can use to deploy resources in the production account.

Note: Verify that in addition to having the necessary permissions to work with CloudFormation, your production identity also has permission to perform the iam:PassRole action with CloudFormation for the role that the preceding template creates.

As you continue to develop your stack, you will need to repeat the steps in the Use Access Analyzer to create a permissions policy and Deploy the CloudFormation role and policy sections of this post in order to make sure that the permissions policy remains up-to-date with the permissions required to deploy your stack.

Considerations

If your CloudFormation template uses custom resources that are backed by AWS Lambda, you should also run Access Analyzer on the IAM role that is created for the Lambda function in order to build an appropriate permissions policy for that role.

To generate a permissions policy for a Lambda service role

  1. Launch the stack in your development AWS account to create the Lamba function’s role.
  2. Make a note of the name of the role that was created.
  3. Destroy the stack in your development AWS account.
  4. Follow the instructions from the Use Access Analyzer to generate a fine-grained identity policy and Review the generated policy sections of this post to create the least-privilege policy for the role, substituting the Lambda function’s role name for CloudFormationDevExecRole.
  5. Build the resulting least-privilege policy into the CloudFormation template as the Lambda function’s permission policy.

Conclusion

IAM Access Analyzer helps generate fine-grained identity policies that you can use to grant CloudFormation the permissions it needs to create, update, and delete resources in your stack. By granting CloudFormation only the necessary permissions, you can incorporate the principle of least privilege, developers can deploy their stacks in production using reduced permissions, and cloud administrators can create guardrails for developers in production settings.

For additional information on applying the principle of least privilege to AWS CloudFormation, see How to implement the principle of least privilege with CloudFormation StackSets.

If you have feedback about this blog post, submit comments in the Comments section below. You can also start a new thread on AWS Identity and Access Management re:Post to get answers from the community.

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Author

Joel Knight

Joel is a Senior Consultant, Infrastructure Architecture, with AWS and is based in Calgary, Canada. When not wrangling infrastructure-as-code templates, Joel likes to spend time with his family and dabble in home automation.

Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

IAM Access Analyzer makes it simpler to author and validate role trust policies

Post Syndicated from Mathangi Ramesh original https://aws.amazon.com/blogs/security/iam-access-analyzer-makes-it-simpler-to-author-and-validate-role-trust-policies/

AWS Identity and Access Management (IAM) Access Analyzer provides many tools to help you set, verify, and refine permissions. One part of IAM Access Analyzer—policy validation—helps you author secure and functional policies that grant the intended permissions. Now, I’m excited to announce that AWS has updated the IAM console experience for role trust policies to make it simpler for you to author and validate the policy that controls who can assume a role. In this post, I’ll describe the new capabilities and show you how to use them as you author a role trust policy in the IAM console.

Overview of changes

A role trust policy is a JSON policy document in which you define the principals that you trust to assume the role. The principals that you can specify in the trust policy include users, roles, accounts, and services. The new IAM console experience provides the following features to help you set the right permissions in the trust policy:

  • An interactive policy editor prompts you to add the right policy elements, such as the principal and the allowed actions, and offers context-specific documentation.
  • As you author the policy, IAM Access Analyzer runs over 100 checks against your policy and highlights issues to fix. This includes new policy checks specific to role trust policies, such as a check to make sure that you’ve formatted your identity provider correctly. These new checks are also available through the IAM Access Analyzer policy validation API.
  • Before saving the policy, you can preview findings for the external access granted by your trust policy. This helps you review external access, such as access granted to a federated identity provider, and confirm that you grant only the intended access when you create the policy. This functionality was previously available through the APIs, but now it’s also available in the IAM console.

In the following sections, I’ll walk you through how to use these new features.

Example scenario

For the walkthrough, consider the following example, which is illustrated in Figure 1. You are a developer for Example Corp., and you are working on a web application. You want to grant the application hosted in one account—the ApplicationHost account—access to data in another account—the BusinessData account. To do this, you can use an IAM role in the BusinessData account to grant temporary access to the application through a role trust policy. You will grant a role in the ApplicationHost account—the PaymentApplication role—to access the BusinessData account through a role—the ApplicationAccess role. In this example, you create the ApplicationAccess role and grant cross-account permissions through the trust policy by using the new IAM console experience that helps you set the right permissions.

Figure 1: Visual explanation of the scenario

Figure 1: Visual explanation of the scenario

Create the role and grant permissions through a role trust policy with the policy editor

In this section, I will show you how to create a role trust policy for the ApplicationAccess role to grant the application access to the data in your account through the policy editor in the IAM console.

To create a role and grant access

  1. In the BusinessData account, open the IAM console, and in the left navigation pane, choose Roles.
  2. Choose Create role, and then select Custom trust policy, as shown in Figure 2.
    Figure 2: Select "Custom trust policy" when creating a role

    Figure 2: Select “Custom trust policy” when creating a role

  3. In the Custom trust policy section, for 1. Add actions for STS, select the actions that you need for your policy. For example, to add the action sts:AssumeRole, choose AssumeRole.
    Figure 3: JSON role trust policy

    Figure 3: JSON role trust policy

  4. For 2. Add a principal, choose Add to add a principal.
  5. In the Add principal box, for Principal type, select IAM roles. This populates the ARN field with the format of the role ARN that you need to add to the policy, as shown in Figure 4.
    Figure 4: Add a principal to your role trust policy

    Figure 4: Add a principal to your role trust policy

  6. Update the role ARN template with the actual account and role information, and then choose Add principal. In our example, the account is ApplicationHost with an AWS account number of 111122223333, and the role is PaymentApplication role. Therefore, the role ARN is arn:aws:iam:: 111122223333: role/PaymentApplication. Figure 5 shows the role trust policy with the action and principal added.
    Figure 5: Sample role trust policy

    Figure 5: Sample role trust policy

  7. (Optional) To add a condition, for 3. Add a condition, choose Add, and then complete the Add condition box according to your needs.

Author secure policies by reviewing policy validation findings

As you author the policy, you can see errors or warnings related to your policy in the policy validation window, which is located below the policy editor in the console. With this launch, policy validation in IAM Access Analyzer includes 13 new checks focused on the trust relationship for the role. The following are a few examples of these checks and how to address them:

  • Role trust policy unsupported wildcard in principal – you can’t use a * in your role trust policy.
  • Invalid federated principal syntax in role trust policy – you need to fix the format of the identity provider.
  • Missing action for condition key – you need to add the right action for a given condition, such as the sts:TagSession when there are session tag conditions.

For a complete list of checks, see Access Analyzer policy check reference.

To review and fix policy validation findings

  1. In the policy validation window, do the following:
    • Choose the Security tab to check if your policy is overly permissive.
    • Choose the Errors tab to review any errors associated with the policy.
    • Choose the Warnings tab to review if aspects of the policy don’t align with AWS best practices.
    • Choose the Suggestions tab to get recommendations on how to improve the quality of your policy.
    Figure 6: Policy validation window in IAM Access Analyzer with a finding for your policy

    Figure 6: Policy validation window in IAM Access Analyzer with a finding for your policy

  2. For each finding, choose Learn more to review the documentation associated with the finding and take steps to fix it. For example, Figure 6 shows the error Mismatched Action For Principal. To fix the error, remove the action sts:AssumeRoleWithWebIdentity.

Preview external access by reviewing cross-account access findings

IAM Access Analyzer also generates findings to help you assess if a policy grants access to external entities. You can review the findings before you create the policy to make sure that the policy grants only intended access. To preview the findings, you create an analyzer and then review the findings.

To preview findings for external access

  1. Below the policy editor, in the Preview external access section, choose Go to Access Analyzer, as shown in Figure 7.

    Note: IAM Access Analyzer is a regional service, and you can create a new analyzer in each AWS Region where you operate. In this situation, IAM Access Analyzer looks for an analyzer in the Region where you landed on the IAM console. If IAM Access Analyzer doesn’t find an analyzer there, it asks you to create an analyzer.

    Figure 7: Preview external access widget without an analyzer

    Figure 7: Preview external access widget without an analyzer

  2. On the Create analyzer page, do the following to create an analyzer:
    • For Name, enter a name for your analyzer.
    • For Zone of trust, select the correct account.
    • Choose Create analyzer.
    Figure 8: Create an analyzer to preview findings

    Figure 8: Create an analyzer to preview findings

  3. After you create the analyzer, navigate back to the role trust policy for your role to review the external access granted by this policy. The following figure shows that external access is granted to PaymentApplication.
    Figure 9: Preview finding

    Figure 9: Preview finding

  4. If the access is intended, you don’t need to take any action. In this example, I want the PaymentApplication role in the ApplicationHost account to assume the role that I’m creating.
  5. If the access is unintended, resolve the finding by updating the role ARN information.
  6. Select Next and grant the required IAM permissions for the role.
  7. Name the role ApplicationAccess, and then choose Save to save the role.

Now the application can use this role to access the BusinessData account.

Conclusion

By using the new IAM console experience for role trust policies, you can confidently author policies that grant the intended access. IAM Access Analyzer helps you in your least-privilege journey by evaluating the policy for potential issues to make it simpler for you to author secure policies. IAM Access Analyzer also helps you preview external access granted through the trust policy to help ensure that the granted access is intended. To learn more about how to preview IAM Access Analyzer cross-account findings, see Preview access in the documentation. To learn more about IAM Access Analyzer policy validation checks, see Access Analyzer policy validation. These features are also available through APIs.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread at AWS IAM re:Post or contact AWS Support.

Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Use IAM Access Analyzer to generate IAM policies based on access activity found in your organization trail

Post Syndicated from Mathangi Ramesh original https://aws.amazon.com/blogs/security/use-iam-access-analyzer-to-generate-iam-policies-based-on-access-activity-found-in-your-organization-trail/

In April 2021, AWS Identity and Access Management (IAM) Access Analyzer added policy generation to help you create fine-grained policies based on AWS CloudTrail activity stored within your account. Now, we’re extending policy generation to enable you to generate policies based on access activity stored in a designated account. For example, you can use AWS Organizations to define a uniform event logging strategy for your organization and store all CloudTrail logs in your management account to streamline governance activities. You can use Access Analyzer to review access activity stored in your designated account and generate a fine-grained IAM policy in your member accounts. This helps you to create policies that provide only the required permissions for your workloads.

Customers that use a multi-account strategy consolidate all access activity information in a designated account to simplify monitoring activities. By using AWS Organizations, you can create a trail that will log events for all Amazon Web Services (AWS) accounts into a single management account to help streamline governance activities. This is sometimes referred to as an organization trail. You can learn more from Creating a trail for an organization. With this launch, you can use Access Analyzer to generate fine-grained policies in your member account and grant just the required permissions to your IAM roles and users based on access activity stored in your organization trail.

When you request a policy, Access Analyzer analyzes your activity in CloudTrail logs and generates a policy based on that activity. The generated policy grants only the required permissions for your workloads and makes it easier for you to implement least privilege permissions. In this blog post, I’ll explain how to set up the permissions for Access Analyzer to access your organization trail and analyze activity to generate a policy. To generate a policy in your member account, you need to grant Access Analyzer limited cross-account access to access the Amazon Simple Storage Service (Amazon S3) bucket where logs are stored and review access activity.

Generate a policy for a role based on its access activity in the organization trail

In this example, you will set fine-grained permissions for a role used in a development account. The example assumes that your company uses Organizations and maintains an organization trail that logs all events for all AWS accounts in the organization. The logs are stored in an S3 bucket in the management account. You can use Access Analyzer to generate a policy based on the actions required by the role. To use Access Analyzer, you must first update the permissions on the S3 bucket where the CloudTrail logs are stored, to grant access to Access Analyzer.

To grant permissions for Access Analyzer to access and review centrally stored logs and generate policies

  1. Sign in to the AWS Management Console using your management account and go to S3 settings.
  2. Select the bucket where the logs from the organization trail are stored.
  3. Change object ownership to bucket owner preferred. To generate a policy, all of the objects in the bucket must be owned by the bucket owner.
  4. Update the bucket policy to grant cross-account access to Access Analyzer by adding the following statement to the bucket policy. This grants Access Analyzer limited access to the CloudTrail data. Replace the <organization-bucket-name>, and <organization-id> with your values and then save the policy.
    {
        "Version": "2012-10-17",
        "Statement": 
        [
        {
            "Sid": "PolicyGenerationPermissions",
            "Effect": "Allow",
            "Principal": {
                "AWS": "*"
            },
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::<organization-bucket-name>",
                "arn:aws:s3:::my-organization-bucket/AWSLogs/o-exampleorgid/${aws:PrincipalAccount}/*
    "
            ],
            "Condition": {
    "StringEquals":{
    "aws:PrincipalOrgID":"<organization-id>"
    },
    
                "StringLike": {"aws:PrincipalArn":"arn:aws:iam::${aws:PrincipalAccount}:role/service-role/AccessAnalyzerMonitorServiceRole*"            }
            }
        }
        ]
    }
    

By using the preceding statement, you’re allowing listbucket and getobject for the bucket my-organization-bucket-name if the role accessing it belongs to an account in your Organizations and has a name that starts with AccessAnalyzerMonitorServiceRole. Using aws:PrincipalAccount in the resource section of the statement allows the role to retrieve only the CloudTrail logs belonging to its own account. If you are encrypting your logs, update your AWS Key Management Service (AWS KMS) key policy to grant Access Analyzer access to use your key.

Now that you’ve set the required permissions, you can use the development account and the following steps to generate a policy.

To generate a policy in the AWS Management Console

  1. Use your development account to open the IAM Console, and then in the navigation pane choose Roles.
  2. Select a role to analyze. This example uses AWS_Test_Role.
  3. Under Generate policy based on CloudTrail events, choose Generate policy, as shown in Figure 1.
     
    Figure 1: Generate policy from the role detail page

    Figure 1: Generate policy from the role detail page

  4. In the Generate policy page, select the time window for which IAM Access Analyzer will review the CloudTrail logs to create the policy. In this example, specific dates are chosen, as shown in Figure 2.
     
    Figure 2: Specify the time period

    Figure 2: Specify the time period

  5. Under CloudTrail access, select the organization trail you want to use as shown in Figure 3.

    Note: If you’re using this feature for the first time: select create a new service role, and then choose Generate policy.

    This example uses an existing service role “AccessAnalyzerMonitorServiceRole_MBYF6V8AIK.”
     

    Figure 3: CloudTrail access

    Figure 3: CloudTrail access

  6. After the policy is ready, you’ll see a notification on the role page. To review the permissions, choose View generated policy, as shown in Figure 4.
     
    Figure 4: Policy generation progress

    Figure 4: Policy generation progress

After the policy is generated, you can see a summary of the services and associated actions in the generated policy. You can customize it by reviewing the services used and selecting additional required actions from the drop down. To refine permissions further, you can replace the resource-level placeholders in the policies to restrict permissions to just the required access. You can learn more about granting fine-grained permissions and creating the policy as described in this blog post.

Conclusion

Access Analyzer makes it easier to grant fine-grained permissions to your IAM roles and users by generating IAM policies based on the CloudTrail activity centrally stored in a designated account such as your AWS Organizations management accounts. To learn more about how to generate a policy, see Generate policies based on access activity in the IAM User Guide.

If you have feedback about this blog post, submit comments in the Comments section below. If you have questions about this blog post, start a new thread on the IAM forum or contact AWS Support.

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Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

Techniques for writing least privilege IAM policies

Post Syndicated from Ben Potter original https://aws.amazon.com/blogs/security/techniques-for-writing-least-privilege-iam-policies/

In this post, I’m going to share two techniques I’ve used to write least privilege AWS Identity and Access Management (IAM) policies. If you’re not familiar with IAM policy structure, I highly recommend you read understanding how IAM works and policies and permissions.

Least privilege is a principle of granting only the permissions required to complete a task. Least privilege is also one of many Amazon Web Services (AWS) Well-Architected best practices that can help you build securely in the cloud. For example, if you have an Amazon Elastic Compute Cloud (Amazon EC2) instance that needs to access an Amazon Simple Storage Service (Amazon S3) bucket to get configuration data, you should only allow read access to the specific S3 bucket that contains the relevant data.

There are a number of ways to grant access to different types of resources, as some resources support both resource-based policies and IAM policies. This blog post will focus on demonstrating how you can use IAM policies to grant restrictive permissions to IAM principals to meet least privilege standards.

In AWS, an IAM principal can be a user, role, or group. These identities start with no permissions and you add permissions using a policy. In AWS, there are different types of policies that are used for different reasons. In this blog, I only give examples for identity-based policies that attach to IAM principals to grant permissions to an identity. You can create and attach multiple identity-based policies to your IAM principals, and you can reuse them across your AWS accounts. There are two types of managed policies. Customer managed policies are created and managed by you, the customer. AWS managed policies are provided as examples, cannot be modified, but can be copied, enhanced, and saved as Customer managed policies. The main elements of a policy statement are:

  • Effect: Specifies whether the statement will Allow or Deny an action.
  • Action: Describes a specific action or actions that will either be allowed or denied to run based on the Effect entered. API actions are unique to each service. For example, s3:ListBuckets is an Amazon S3 service API action that enables an IAM Principal to list all S3 buckets in the same account.
  • NotAction: Can be used as an alternative to using Action. This element will allow an IAM principal to invoke all API actions to a specific AWS service except those actions specified in this list.
  • Resource: Specifies the resources—for example, an S3 bucket or objects—that the policy applies to in Amazon Resource Name (ARN) format.
  • NotResource: Can be used instead of the Resource element to explicitly match every AWS resource except those specified.
  • Condition: Allows you to build expressions to match the condition keys and values in the policy against keys and values in the request context sent by the IAM principal. Condition keys can be service-specific or global. A global condition key can be used with any service. For example, a key of aws:CurrentTime can be used to allow access based on date and time.

Starting with the visual editor

The visual editor is my default starting place for building policies as I like the wizard and seeing all available services, actions, and conditions without looking at the documentation. If there is a complex policy with many services, I often look at the AWS managed policies as a starting place for the actions that are required, then use the visual editor to fine tune and check the resources and conditions.

The policy I’m going to walk you through creating is to grant an AWS Lambda function permission to get specific objects from Amazon S3, and put items in a specific table in Amazon DynamoDB. You can access the visual editor when you choose Create policy under policies in the IAM console, or add policies when viewing a role, group, or user as shown in Figure 1. If you’re not familiar with creating policies, you can follow the full instructions in the IAM documentation.

Figure 1: Use the visual editor to create a policy

Figure 1: Use the visual editor to create a policy

Begin by choosing the first service—S3—to grant access to as shown in Figure 2. You can only choose one service at a time, so you’ll need to add DynamoDB after.

Figure 2: Select S3 service

Figure 2: Select S3 service

Now you will see a list of access levels with the option to manually add actions. Expand the read access level to show all read actions that are supported by the Amazon S3 service. You can now see all read access level actions. For getting an object, check the box for GetObject. Selecting the ? next to an action expands information including a description, supported resource types, and supported condition keys as shown in Figure 3.

Figure 3: Expand Read in Access level, select GetObject, and select the ? next to GetObject

Figure 3: Expand Read in Access level, select GetObject, and select the ? next to GetObject

Expand Resources, you will see that the visual editor has listed object as that is the only resource supported by the GetObject action as shown in Figure 4.

Figure 4: Expand Resources

Figure 4: Expand Resources

Select Add ARN, which opens a dialogue to help you specify the ARN for the objects. Enter a bucket name—such as doc-example-bucket—and then the object name. For the object name you can use a wildcard (*) as a suffix. For example, to allow objects beginning with alpha you would enter alpha*. This is an important step. For this least privileged policy, you are restricting to a specific bucket, and an object prefix. You could even specify an individual object depending on your use case.

Figure 5: Enter bucket name and object name

Figure 5: Enter bucket name and object name

If you have multiple ARNs (bucket and objects) to allow, you can repeat the step.

Figure 6: ARN added for S3 object

Figure 6: ARN added for S3 object

The final step is to expand the request conditions, and choose Add condition. The Add request condition dialogue will open. Select the drop down next to Condition key to list the global condition keys, then the service level condition keys are listed after. You’ll see that there’s an s3:ExistingObjectTag condition that—as the name suggests—matches an existing object tag. You can use this condition key to allow the GetObject request only when the object tag meets your condition. That means you can tag your objects with a specific tag key and value pair, and your policy condition must match this key-value pair to allow the action to execute. When you’re using condition keys with multiple keys or values, you can use condition operators and evaluation logic. As shown in Figure 7, tag-key is entered directly below the condition key. This is the key of the tag to match. For the Operator, select StringEquals to match the tag exactly. Checking If exists tests at least one member of the set of request values, and at least one member of the set of condition key values. The Value to enter is the actual tag value: tag-value as shown in figure 7.

Figure 7: ARN added for S3 object

Figure 7: ARN added for S3 object

That’s it for adding the S3 action, as shown in figure 8.

Figure 8: S3 GetObject action with resource and conditions configured

Figure 8: S3 GetObject action with resource and conditions configured

Now you need to add the DynamoDB permissions by selecting Add additional permissions. Select Choose a service and then select DynamoDB. For actions, expand the Write access level, then choose PutItem.

Figure 9: Choose write access level

Figure 9: Choose write access level

Expand Resources and then select Add ARN. The dialogue that appears will help you build the ARN just like it did for the Amazon S3 service. Enter the Region, for example the ap-southeast-2 (Sydney) Region, the account ID, and the table name. Choosing Add will add the resource ARN to your policy.

Figure 10: Enter Region, account, and table name

Figure 10: Enter Region, account, and table name

Now it’s time to add conditions. Expand Request conditions and then choose Add condition.

There are many DynamoDB conditions that you could use, however you can choose dynamodb:LeadingKeys to represent the first key, or partition keys in a table. You can see from the documentation that a qualifier of For all values in request is recommend. For the Operator you can use StringEquals as your string is going to exactly match, then a Value can use a prefix with wildcard, such as alpha* as shown in figure 11.

Figure 11: Add request conditions

Figure 11: Add request conditions

Choosing Add will take you back to the main visual editor where you can choose Review policy to continue. Enter a name and description for the policy, and then choose Create policy.

You can now attach it to a role to test.

You can see in this example that a policy can use least privilege by using specific resources and conditions. Note that sometimes when you use the AWS Management Console, it requires additional permissions to provide information for the console experience.

Starting with AWS managed policies

AWS managed policies can be a good starting place to see the actions typically associated with a particular service or job function. For example, you can attach the AmazonS3ReadOnlyAccess policy to a role used by an Amazon EC2 instance that allows read-only access to all Amazon S3 buckets. It has an effect of Allow to allow access, and there are two actions that use wildcards (*) to allow all Get and List actions for S3—for example, s3:GetObject and s3:ListBuckets. The resource is a wildcard to allow all S3 buckets the account has access to. A useful feature of this policy is that it only allows read and list access to S3, but not to any other services or types of actions.

Let’s make our own custom IAM policy to make it least privilege. Starting with the action element, you can use the reference for Amazon S3 to see all actions, a description of what each action does, the resource type for each action, and condition keys for each action. Now let’s imagine this policy is used by an Amazon EC2 instance to fetch an application configuration object from within an S3 bucket. Looking at the descriptions for actions starting with Get you can see that the only action that we really need is GetObject. You can then use the resource element to restrict an action to a set of objects prefixed with config within a specific bucket.

         "Effect": "Allow",
         "Action": "s3:GetObject",
         "Resource": "arn:aws:s3::: <doc-example-bucket>/<config*>"

Now that you’ve reduced the scope of what this policy can do for service actions and resources, you can add a condition element that uses attribute based access control (ABAC) to define conditions based on attributes—in this case, a resource tag. In this example, when you’re reading objects from a single bucket, you can set specific conditions to further reduce the scope of permissions given to an IAM principal. There’s an s3:ExistingObjectTag condition that you can use to allow the GetObject request only when the object tag meets your condition. That means you can tag your objects with a specific tag key and value pair, and your IAM policy condition must match this key-value pair to allow the API action to successfully run. When you’re using condition keys with multiple keys or values, you can use condition operators and evaluation logic. You can see that ForAnyValue tests at least one member of the set of request values, and at least one member of the set of condition key values. Alternatively, you can use global condition keys that apply to all services:

         "Effect": "Allow",
         "Action": "s3:GetObject",
         "Resource": "arn:aws:s3:::<doc-example-bucket>/<config*>",
         "Condition": {
                "ForAnyValue:StringEquals": {
                    "s3:ExistingObjectTag/<tag-key>": "<tag-value>"
            }

In the preceding policy example, the condition element only allows s3:GetObject permissions if the object is tagged with a key of tag-key and a value of tag-value. While you’re experimenting, you can identify errors in your custom policies by using the IAM policy simulator or reviewing the errors messages recorded in AWS CloudTrail logs.

Conclusion

In this post, I’ve shown two different techniques that you can use to create least privilege policies for IAM. You can adapt these methods to create AWS Single Sign-On permission sets and AWS Organizations service control policies (SCPs). Starting with managed policies is a useful strategy when an AWS supplied managed policy already exists for your use case, and then to reduce the scope of what it can do through permissions. I tend to use the visual editor the most for editing policies because it saves looking up the resource and conditions for each action. I suggest that you start by reviewing the policies you’re already using. Start with policies that grant excessive permissions—like the example Administrator policy—and tie them back to the use case of the users or things that need the access. Use the last accessed information, IAM best practices, and look at the AWS Well-Architected best practices and AWS Well-Architected tool.

If you have feedback about this post, submit comments in the Comments section below.

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Author

Ben Potter

Ben is the global security leader for the AWS Well-Architected Framework and is responsible for sharing best practices in security with customers and partners. Ben is also an ambassador for the No More Ransom initiative helping fight cyber crime with Europol, McAfee, and law enforcement across the globe. You can learn more about him in this interview.

New! Streamline existing IAM Access Analyzer findings using archive rules

Post Syndicated from Andrea Nedic original https://aws.amazon.com/blogs/security/new-streamline-existing-iam-access-analyzer-findings-using-archive-rules/

AWS Identity and Access Management (IAM) Access Analyzer generates comprehensive findings to help you identify resources that grant public and cross-account access. Now, you can also apply archive rules to existing findings, so you can better manage findings and focus on the findings that need your attention most.

You can think of archive rules as similar to email rules. You define email rules to automatically organize emails. With IAM Access Analyzer, you can define archive rules to automatically mark findings as intended access. Now, those rules can apply to existing as well as new IAM Access Analyzer findings. This helps you focus on findings for potential unintended access to your resources. You can then easily track and resolve these findings by reducing access, helping you to work towards least privilege.

In this post, first I give a brief overview of IAM Access Analyzer. Then I show you an example of how to create an archive rule to automatically archive findings for intended access. Finally, I show you how to update an archive rule to mark existing active findings as intended.

IAM Access Analyzer overview

IAM Access Analyzer helps you determine which resources can be accessed publicly or from other accounts or organizations. IAM Access Analyzer determines this by mathematically analyzing access control policies attached to resources. This form of analysis—called automated reasoning—applies logic and mathematical inference to determine all possible access paths allowed by a resource policy. This is how IAM Access Analyzer uses provable security to deliver comprehensive findings for potential unintended bucket access. You can enable IAM Access Analyzer in the IAM console by creating an analyzer for an account or an organization. Once you’ve created your analyzer, you can review findings for resources that can be accessed publicly or from other AWS accounts or organizations.

Create an archive rule to automatically archive findings for intended access

When you review findings and discover common patterns for intended access, you can create archive rules to automatically archive those findings. This helps you focus on findings for unintended access to your resources, just like email rules help streamline your inbox.

To create an archive rule

In the IAM console, choose Archive rules under Access Analyzer. Then, choose Create archive rule to display the Create archive rule page shown in Figure 1. There, you find the option to name the rule or use the name generated by default. In the Rule section, you define criteria to match properties of findings you want to archive. Just like email rules, you can add multiple criteria to the archive rule. You can define each criterion by selecting a finding property, an operator, and a value. To help ensure a rule doesn’t archive findings for public access, the criterion Public access is false is suggested by default.
 

Figure 1: IAM Access Analyzer create archive rule page where you add criteria to create a new archive rule

Figure 1: IAM Access Analyzer create archive rule page where you add criteria to create a new archive rule

For example, I have a security audit role external to my account that I expect to have access to resources in my account. To mark that access as intended, I create a rule to archive all findings for Amazon S3 buckets in my account that can be accessed by the security audit role outside of the account. To do this, I include two criteria: Resource type matches S3 bucket, and the AWS Account value matches the security audit role ARN. Once I add these criteria, the Results section displays the list of existing active findings the archive rule matches, as shown in Figure 2.
 

Figure 2: A rule to archive all findings for S3 buckets in an account that can be accessed by the audit role outside of the account, with matching findings displayed

Figure 2: A rule to archive all findings for S3 buckets in an account that can be accessed by the audit role outside of the account, with matching findings displayed

When you’re done adding criteria for your archive rule, select Create and archive active findings to archive new and existing findings based on the rule criteria. Alternatively, you can choose Create rule to create the rule for new findings only. In the preceding example, I chose Create and archive active findings to archive all findings—existing and new—that match the criteria.

Update an archive rule to mark existing findings as intended

You can also update an archive rule to archive existing findings retroactively and streamline your findings. To edit an archive rule, choose Archive rules under Access Analyzer, then select an existing rule and choose Edit. In the Edit archive rule page, update the archive rule criteria and review the list of existing active findings the archive rule applies to. When you save the archive rule, you can apply it retroactively to existing findings by choosing Save and archive active findings as shown in Figure 3. Otherwise, you can choose Save rule to update the rule and apply it to new findings only.

Note: You can also use the new IAM Access Analyzer API operation ApplyArchiveRule to retroactively apply an archive rule to existing findings that meet the archive rule criteria.

 

Figure 3: IAM Access Analyzer edit archive rule page where you can apply the rule retroactively to existing findings by choosing Save and archive active findings

Figure 3: IAM Access Analyzer edit archive rule page where you can apply the rule retroactively to existing findings by choosing Save and archive active findings

Get started

To turn on IAM Access Analyzer at no additional cost, open the IAM console. IAM Access Analyzer is available at no additional cost in the IAM console and through APIs in all commercial AWS Regions, AWS China Regions, and AWS GovCloud (US). To learn more about IAM Access Analyzer and which resources it supports, visit the feature page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS IAM forum or contact AWS Support.

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Author

Andrea Nedic

Andrea is a Sr. Tech Product Manager for AWS Identity and Access Management. She enjoys hearing from customers about how they build on AWS. Outside of work, Andrea likes to ski, dance, and be outdoors. She holds a PhD from Princeton University.

How to get read-only visibility into the AWS Control Tower console

Post Syndicated from Bruno Mendez original https://aws.amazon.com/blogs/security/how-to-get-read-only-visibility-into-aws-control-tower-console/

When you audit an environment governed by AWS Control Tower, having visibility into the AWS Control Tower console allows you to collect important configuration information, but currently there isn’t a read-only role installed by AWS Control Tower. In this post, I will show you how to create a custom permission set by using both a managed AWS policy and a custom permissions policy. This custom permission set will allow you to get the visibility you need, while still enforcing the principle of least privilege. You will have access to the read-only information you need, without asking your administrator to provide the attestation.

AWS Control Tower sets up AWS Single Sign-On (AWS SSO) with a native default directory. AWS Control Tower comes with a set of preconfigured permission sets available out-of-the-box. A permission set is a collection of administrator-defined policies that AWS SSO uses to determine a user’s effective permissions to access a specific AWS account. Permission sets can contain an AWS inline policy and you can also attach AWS managed policies. When you assign a permission set to a user or group in an account, AWS SSO creates an IAM role in the AWS account, configures the inline and AWS managed policies, and creates the trust policies that allow the assigned users to assume the role through AWS SSO.

To learn more about inline and AWS managed policies, see Managed Policies and Inline Policies and the IAM User Guide on AWS managed policies for job functions.

To create a custom permission set for AWS Control Tower

  1. Log into your AWS Control Tower environment as an administrator.
  2. Choose the AWS Single Sign-On service, then choose AWS accounts.
  3. On the AWS Accounts pane, choose the Permission sets tab, then choose Create permission set, as shown in the following figure.

    Figure 1: Permission sets tab in the SSO console

    Figure 1: Permission sets tab in the SSO console

  4. Select Create a custom permission set and enter a name in the Name field (in this example, I named mine Audit-enhanced), then enter text in the Description field, as shown in figure 2.

    Figure 2: AWS Single Sign-On console – Create new permission set workflow

    Figure 2: AWS Single Sign-On console – Create new permission set workflow

  5. Choose a value for Session duration (in this example I set the duration to 1 hour). Optionally, you can set a relay state (in this example, I left it blank), and select both Attach AWS managed policies and Create a custom permissions policy, as shown in the following figure.

    Figure 3: AWS Single Sign-On console – Setting additional permission set configurations

    Figure 3: AWS Single Sign-On console – Setting additional permission set configurations

  6. In the Attach AWS Managed policies dashboard, in the search bar, enter audit and select the SecurityAudit managed policy, as shown in figure 4.

    Figure 4: AWS Single Sign-On console – Attaching AWS managed policy

    Figure 4: AWS Single Sign-On console – Attaching AWS managed policy

  7. Copy the following JSON policy to your clipboard.
    
    {
                "Version": "2012-10-17",
                "Statement": [
                    {
                        "Effect": "Allow",
                        "Action": [
                          "controltower:Get*",
                          "controltower:List*",
                          "controltower:Describe*",
                          "sso:getpermissionset",
                          "sso:DescribeRegisteredRegions",
                          "sso:ListDirectoryAssociations",
                          "sso-directory:DescribeDirectory"		
                        ],
                        "Resource": "*"
                    }
                ]
         }
    

    This policy grants the following read-level permissions: Get, List, Describe API actions. This is the additional set of permissions necessary to enhance the SecurityAudit role, so that you can gain visibility into the AWS Control Tower console.

  8. Scroll down to the Create a custom permissions policy dashboard, paste the policy you previously copied into the field, as shown in figure 5, then choose Create.

    Figure 5: AWS Single Sign-On console – Entering JSON code for custom permission policy

    Figure 5: AWS Single Sign-On console – Entering JSON code for custom permission policy

Now, when you go to the Permission sets tab, you should see your newly created custom permission set.

To assign the newly created permission set access to your AWS Control Tower master account

  1. On the AWS organization tab, select the box for your AWS Control Tower master account (in this example, the account newControlTower), then choose Assign users, as shown in figure 6.

    Figure 6: AWS Single Sign-On console – AWS organization tab – Assign access workflow

    Figure 6: AWS Single Sign-On console – AWS organization tab – Assign access workflow

  2. On the Users tab, select your user (in this example, CT Tester) as shown in figure 7, and choose Next: Permission sets.

    Figure 7: AWS Single Sign-On console – Users tab – Assigning access to your user

    Figure 7: AWS Single Sign-On console – Users tab – Assigning access to your user

  3. Select the box next to the custom permission set you created earlier (in this example, Audit-enhanced), and choose Finish, as shown in figure 8.

    Figure 8: AWS Single Sign-On console – Select permission sets

    Figure 8: AWS Single Sign-On console – Select permission sets

You should see a Complete page, and the newControlTower account will show Status as Complete, as shown in figure 9.

Figure 9: AWS Single Sign-On console – Successful completion of permission set assignment

Figure 9: AWS Single Sign-On console – Successful completion of permission set assignment

You now have a permission set that enhances your SecurityAuditor role and gives you read-only visibility into your AWS Control Tower environment.

Summary

In this post, we’ve detailed how to enhance an “audit-like” role to incorporate additional permissions by using a custom permission set in AWS SSO, while enforcing the principle of least privilege to gain read-only capabilities into the AWS Control Tower console.

For more information on the technologies mentioned in this post, see the following links:

If you have feedback about this post, submit comments in the Comments section below.

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Author

Bruno Mendez

Bruno joined AWS as a Security Consultant in 2019 and has since worked with several global customers to enable and strengthen their cloud security posture as they embarked in their cloud transformational journeys. Bruno enjoys architecting, assessing, automating, improving, and discussing security. Outside of work Bruno loves playing soccer on the weekends and spending time with the family.