All posts by Noelle Kagan

Unmasking the Unseen: Your Guide to Taming Shadow AI with Cloudflare One

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/shadow-AI-analytics/

The digital landscape of corporate environments has always been a battleground between efficiency and security. For years, this played out in the form of “Shadow IT” — employees using unsanctioned laptops or cloud services to get their jobs done faster. Security teams became masters at hunting these rogue systems, setting up firewalls and policies to bring order to the chaos.

But the new frontier is different, and arguably far more subtle and dangerous.

Imagine a team of engineers, deep into the development of a groundbreaking new product. They’re on a tight deadline, and a junior engineer, trying to optimize his workflow, pastes a snippet of a proprietary algorithm into a popular public AI chatbot, asking it to refactor the code for better performance. The tool quickly returns the revised code, and the engineer, pleased with the result, checks it in. What they don’t realize is that their query, and the snippet of code, is now part of the AI service’s training data, or perhaps logged and stored by the provider. Without anyone noticing, a critical piece of the company’s intellectual property has just been sent outside the organization’s control, a silent and unmonitored data leak.

This isn’t a hypothetical scenario. It’s the new reality. Employees, empowered by these incredibly powerful AI tools, are now using them for everything from summarizing confidential documents to generating marketing copy and, yes, even writing code. The data leaving the company in these interactions is often invisible to traditional security tools, which were never built to understand the nuances of a browser tab interacting with a large language model. This quiet, unmanaged usage is “Shadow AI,” and it represents a new, high-stakes security blind spot.

To combat this, we need a new approach—one that provides visibility into this new class of applications and gives security teams the control they need, without impeding the innovation that makes these tools so valuable.

Shadow AI reporting

This is where the Cloudflare Shadow IT Report comes in. It’s not a list of threats to be blocked, but rather a visibility and analytics tool designed to help you understand the problem before it becomes a crisis. Instead of relying on guesswork or trying to manually hunt down every unsanctioned application, Cloudflare One customers can use the insights from their traffic to gain a clear, data-driven picture of their organization’s application usage.

The report provides a detailed, categorized view of your application activity, and is easily narrowed down to AI activity. We’ve leveraged our network and threat intelligence capabilities to identify and classify AI services, identifying general-purpose models like ChatGPT, code-generation assistants like GitHub Copilot, and specialized tools used for marketing, data analysis, or other content creation, like Leonardo.ai. This granular view allows security teams to see not just that an employee is using an AI app, but which AI app, and what users are accessing it.

How we built it

Sharp eyed users may have noticed that we’ve had a shadow IT feature for a while — so what changed? While Cloudflare Gateway, our secure web gateway (SWG), has recorded some of this data for some time, users have wanted deeper insights and reporting into their organization’s application usage. Cloudflare Gateway processes hundreds of millions of rows of app usage data for our biggest users daily, and that scale was causing issues with queries into larger time windows. Additionally, the original implementation lacked the filtering and customization capabilities to properly investigate the usage of AI applications. We knew this was information that our customers loved, but we weren’t doing a good enough job of showing it to them.

Solving this was a cross-team effort requiring a complete overhaul by our analytics and reporting engineers. You may have seen our work recently in this July 2025 blog post detailing how we adopted TimescaleDB to support our analytics platform, unlocking our analytics, allowing us to aggregate and compress long term data to drastically improve query performance. This solves the issue we originally faced around our scale, letting our biggest customers query their data for long time periods. Our crawler collects the original HTTP traffic data from Gateway, which we store into a Timescale database.

Once the data are in our database, we built specific, materialized views in our database around the Shadow IT and AI use case to support analytics for this feature. Whereas the existing HTTP analytics we built are centered around the HTTP requests on an account, these specific views are centered around the information relevant to applications, for example: Which of my users are going to unapproved applications? How much bandwidth are they consuming? Is there an end-user in an unexpected geographical location interacting with an unreviewed application? What devices are using the most bandwidth?

Over the past year, the team has defined a set framework for the analytics we surface. Our timeseries graphs and top-n graphs are all filterable by duration and the relevant data points shown, allowing users to drill down to specific data points and see the details of their corporate traffic. We overhauled Shadow IT by examining the data we had and researching how AI applications were presenting visibility challenges for customers. From there we leveraged our existing framework and built the Shadow IT dashboard. This delivered the application-level visibility that we know our customers needed.

How to use it

1. Proxy your traffic with Gateway

The core of the system is Cloudflare Gateway, an in-line filter and proxy for all your organization’s Internet traffic, regardless of where your users are. When an employee tries to access an AI application, their traffic flows through Cloudflare’s global network. Cloudflare can inspect the traffic, including the hostname, and map the traffic to our application definitions. TLS inspection is optional for Gateway customers, but it is required for ShadowIT analytics.

Interactions are logged and tied to user identity, device posture, bandwidth consumed and even the geographic location. This rich context is crucial for understanding who is using which AI tools, when, and from where.

2. Review application use

All this granular data is then presented in an our Shadow IT Report within your Cloudflare One dashboard. Simply filter for AI applications so you can:

  • High-Level Overview: Get an immediate sense of your organization’s AI adoption. See the top AI applications in use, overall usage trends, and the volume of data being processed. This will help you identify and target your security and governance efforts.

  • Granular Drill-Downs: Need more detail? Click on any AI application to see specific users or groups accessing it, their usage frequency, location, and the amount of data transferred. This detail helps you pinpoint teams using AI around the company, as well as how much data is flowing to those applications.


ShadowIT analytics dashboard

3. Mark application approval statuses

We understand that not all AI tools are created equal, and your organization’s comfort level will vary. The Shadow AI Report introduces a flexible framework for Application Approval Status, allowing you to formally categorize each detected AI application:

  • Approved: These are the AI applications that have passed your internal security vetting, comply with your policies, and are officially sanctioned for use. 

  • Unapproved: These are the red-light applications. Perhaps they have concerning data privacy policies, a history of vulnerabilities, or simply don’t align with your business objectives.

  • In Review: For those gray-area applications, or newly discovered tools, this status lets your teams acknowledge their usage while conducting thorough due diligence. It buys you time to make an informed decision without immediate disruption.


Review and mark application statuses in the dashboard

4. Enforce policies

These approval statuses come alive when integrated with Cloudflare Gateway policies. This allows you to automatically enforce your AI decisions at the edge of Cloudflare’s network, ensuring consistent security for every employee, anywhere they work.

Here’s how you can translate your decisions into inline protection:

  • Block unapproved AI: The simplest and most direct action. Create a Gateway HTTP policy that blocks all traffic to any AI application marked as “Unapproved.” This immediately shuts down risky data exfiltration.

  • Limit “In Review” exposure: For applications still being assessed, you might not want a hard block, but rather a soft limit on potential risks:

  • Data Loss Prevention (DLP): Cloudflare DLP inspects and analyzes traffic for indicators of sensitive data (e.g., credit card numbers, PII, internal project names, source code) and can then block the transfer. By applying DLP to “In Review” AI applications, you can prevent AI prompts containing this proprietary data, as well as notify the user why the prompt was blocked. This could have saved our poor junior engineer from their well-intended mistake.. 

  • Restrict Specific Actions: Block only file uploads allowing basic interaction but preventing mass data egress. 

  • Isolate Risky Sessions: Route traffic for “In Review” applications through Cloudflare’s Browser Isolation. Browser Isolation executes the browser session in a secure, remote container, isolating all data interactions from your corporate network. With it, you can control file uploads, clipboard actions, reduce keyboard inputs and more, reducing interaction with the application while you review it.

  • Audit “Approved” usage: Even for AI tools you trust, you might want to log all interactions for compliance auditing or apply specific data handling rules to ensure ongoing adherence to internal policies.

This workflow enables your team to consistently audit your organization’s AI usage and easily update policies to quickly and easily reduce security risk.

Forensics with Cloudflare Log Explorer

While the Shadow AI Report provides excellent insights, security teams often need to perform deeper forensic investigations. For these advanced scenarios, we offer Cloudflare Log Explorer.

Log Explorer allows you to store and query your Cloudflare logs directly within the Cloudflare dashboard or via API, eliminating the need to send massive log volumes to third-party SIEMs for every investigation. It provides raw, unsampled log data with full context, enabling rapid and detailed analysis.

Log Explorer customers can dive into Shadow AI logs with pre-populated SQL queries from Cloudflare Analytics, enabling deeper investigations into AI usage:


Log Search’s SQL query interface

How to investigate Shadow AI with Log Explorer:

  • Trace Specific User Activity: If the Shadow AI Report flags a user with high activity on an “In Review” or “Unapproved” AI app, you can jump into Log Explorer and query by user, application category, or specific AI services. 

  • Analyze Data Exfiltration Attempts: If you have DLP policies configured, you can search for DLP matches in conjunction with AI application categories. This helps identify attempts to upload sensitive data to AI applications and pinpoint exactly what data was being transmitted.

  • Identify Anomalous AI Usage: The Shadow AI Report might show a spike in usage for a particular AI application. In Log Explorer, you can filter by application status (In Review or Unapproved) for a specific time range. Then, look for unusual patterns, such as a high number of requests from a single source IP address, or unexpected geographic origins, which could indicate compromised accounts or policy evasion attempts.

If AI visibility is a challenge for your organization, the Shadow AI Report is available now for Cloudflare One customers, as part of our broader shadow IT discovery capabilities. Log in to your dashboard to start regaining visibility and shaping your AI governance strategy today. 

Ready to modernize how you secure access to AI apps? Reach out for a consultation with our Cloudflare One security experts about how to regain visibility and control. 

Or if you’re not ready to talk to someone yet,  nearly every feature in Cloudflare One is available at no cost for up to 50 users. Many of our largest enterprise customers start by exploring the products themselves on our free plan, and you can get started here.

If you’ve got feedback or want to help shape how Cloudflare enhances visibility across shadow AI, please consider joining our user research program. 

Protect against identity-based attacks by sharing Cloudflare user risk scores with Okta

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/protect-against-identity-based-attacks-by-sharing-cloudflare-user-risk-with-okta

Cloudflare One, our secure access service edge (SASE) platform, is introducing a new integration with Okta, the identity and access management (IAM) vendor, to share risk indicators in real-time and simplify how organizations can dynamically manage their security posture in response to changes across their environments.

For many organizations, it is becoming increasingly challenging and inefficient to adapt to risks across their growing attack surface. In particular, security teams struggle with multiple siloed tools that fail to share risk data effectively with each other, leading to excessive manual effort to extract signals from the noise. To address this complexity, Cloudflare launched risk posture management capabilities earlier this year to make it easier for organizations to accomplish three key jobs on one platform:

  1. Evaluating risk posed by people by using first-party user entity and behavior analytics (UEBA) models

  2. Exchanging risk telemetry with best-in-class security tools, and

  3. Enforcing risk controls based on those dynamic first- and third-party risk scores.

Today’s announcement builds on these capabilities (particularly job #2) and our partnership with Okta by enabling organizations to share Cloudflare’s real-time user risk scores with Okta, which can then automatically enforce policies based on that user’s risk. In this way, organizations can adapt to evolving risks in less time with less manual effort.

Cloudflare’s user risk scoring

Introduced earlier this year, Cloudflare’s user risk scoring analyzes real-time telemetry of user activities and behaviors and assigns a risk score of high, medium, or low. For example, if Cloudflare detects risky or suspicious activity from a user — such as impossible travel, where a user logs in from multiple geographically dispersed locations within a short time frame, data loss prevention (DLP) detections, or endpoint detections suggesting that the device is infected — the user’s risk score will increase. The activity leading to that scoring is logged for analysis.

Cloudflare includes predefined risk behaviors to help you get started. Administrators can create policies based on specific risk behaviors and adjust the risk level for each behavior based on their company’s tolerance.

Share risk scores with Okta and take action automatically

Customers that opt in to this new integration will be able to share continually updated Cloudflare user risk scores with Identity Threat Protection with Okta AI. If a user is deemed too risky, Okta will automatically take action to mitigate the risk, such as enforcing multi-factor authentication (MFA) verification or universally logging the user out from all applications. 

For example, a user has a low risk score from Cloudflare that was shared with Okta, but after exhibiting “impossible travel” behavior, the user’s risk level is raised to high. Cloudflare sends the updated score to Okta, which triggers a Universal Logout and an MFA challenge if the user attempts to log in again. Access to sensitive systems may be revoked completely until the user is verified. 

How it works: continuous risk evaluation and exchange


Figure 1. Diagram showing risky behavior by a user, resulting in sign-out.

We begin by detecting risky behavior from a user (such as an “impossible travel” event between two geographic locations). Instances of risky behavior are called Risk Events. We perform two actions when we observe a Risk Event: logging the event and evaluating whether further action is required. For customers that have enabled Risk Score Sharing with Okta, any change in Risk Score is transmitted to Okta’s Identity Threat Protection (ITP).

Upon receiving a new event, Okta evaluates the change in user risk against the organization’s policies. These policies may include actions such as re-authenticating the user if they become high risk.

When we design new features, we aim for them to be extensible across the industry. For this reason, we chose the OpenID Shared Signals Framework Specification (SSF) to be the foundation of our transmission format. By doing this, we are able to leverage current and future providers that support the standard. The core functionality of SSF revolves around sharing Security Event Tokens (SETs), a specialized version of a JSON Web Token (JWT). Providers can produce and consume Security Event Tokens, forming a “network” of shared user risk information between providers.


Figure 2. Diagram showing a Security Event Token being transmitted from Cloudflare to Okta.

The diagram above (Figure 2) details the process of sharing risk. When sharing Risk Score changes with Okta, we bundle metadata about the risk event and user into the body of a Security Event Token. Following this, the JWT/SET is signed using our private key. This is an important step, as the signature is used to verify the sender’s identity (cryptographic authenticity) and that the payload body has not been tampered with (cryptographic integrity). In plain terms, this signature is used by Okta to verify that the event is unaltered and was sent by Cloudflare.

Once Okta has verified the authenticity and integrity of the SET token, they may use the risk metadata within the body to execute Identity Threat Protection policies defined by the customer. These policies could include actions such as “if a high risk score is received from Cloudflare, sign out the offending user”.

Learn more about the Shared Signals Framework and CAEP in Okta’s announcement blog post.

Get started today

Cloudflare customers can easily enable risk score sharing from the Cloudflare One SSO setup page. This is available to customers whether you’ve already integrated with Okta or are setting up the integration for the first time. You will also be able to confirm that the feature was enabled in your audit logs.

If you’ve already integrated Okta within your Cloudflare One dashboard:

  1. As an admin, navigate to Settings > Authentication and select the Okta login method.

  2. Select “send risk score to Okta.”

If you haven’t yet integrated Okta within your Cloudflare One dashboard:

  1. As an admin, navigate to Settings > Authentication and select a new login method.

  2. Follow the instructions to add Okta as an SSO.

  3. Select “send risk score to Okta.”

Now, whenever a user’s risk score changes within the organization, information is sent to Okta automatically and an audit log is documented.

Uphold Zero Trust principles

In conclusion, the ability to incorporate rich context is essential for making accurate and informed access decisions. With vast amounts of data — including user logins, logouts, websites visited, and emails sent — human analysts would struggle to keep pace with modern security challenges. Cloudflare provides context in the form of a risk score, enabling Okta’s risk engine to make more informed policy decisions about users. This sharing of information powers the continuous evaluation required to enforce Zero Trust policies within your organization, ultimately strengthening your organization’s security posture.

Not yet a Cloudflare One customer? Reach out for a consultation or contact your account manager.

Cloudflare acquires Kivera to add simple, preventive cloud security to Cloudflare One

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/cloudflare-acquires-kivera

We’re excited to announce that Kivera, a cloud security, data protection, and compliance company, has joined Cloudflare. This acquisition extends our SASE portfolio to incorporate inline cloud app controls, empowering Cloudflare One customers with preventative security controls for all their cloud services.

In today’s digital landscape, cloud services and SaaS (software as a service) apps have become indispensable for the daily operation of organizations. At the same time, the amount of data flowing between organizations and their cloud providers has ballooned, increasing the chances of data leakage, compliance issues, and worse, opportunities for attackers. Additionally, many companies — especially at enterprise scale — are working directly with multiple cloud providers for flexibility based on the strengths, resiliency against outages or errors, and cost efficiencies of different clouds. 

Security teams that rely on Cloud Security Posture Management (CSPM) or similar tools for monitoring cloud configurations and permissions and Infrastructure as code (IaC) scanning are falling short due to detecting issues only after misconfigurations occur with an overwhelming volume of alerts. The combination of Kivera and Cloudflare One puts preventive controls directly into the deployment process, or ‘inline’, blocking errors before they happen. This offers a proactive approach essential to protecting cloud infrastructure from evolving cyber threats, maintaining data security, and accelerating compliance. 

An early warning system for cloud security risks 

In a significant leap forward in cloud security, the combination of Kivera’s technology and Cloudflare One adds preventive, inline controls to enforce secure configurations for cloud resources. By inspecting cloud API traffic, these new capabilities equip organizations with enhanced visibility and granular controls, allowing for a proactive approach in mitigating risks, managing cloud security posture, and embracing a streamlined DevOps process when deploying cloud infrastructure.

Kivera will add the following capabilities to Cloudflare’s SASE platform:

  • One-click security: Customers benefit from immediate prevention of the most common cloud breaches caused by misconfigurations, such as accidentally allowing public access or policy inconsistencies.

  • Enforced cloud tenant control: Companies can easily draw boundaries around their cloud resources and tenants to ensure that sensitive data stays within their organization. 

  • Prevent data exfiltration: Easily set rules to prevent data being sent to unauthorized locations.

  • Reduce ‘shadow’ cloud infrastructure: Ensure that every interaction between a customer and their cloud provider is in line with preset standards. 

  • Streamline cloud security compliance: Customers can automatically assess and enforce compliance against the most common regulatory frameworks.

  • Flexible DevOps model: Enforce bespoke controls independent of public cloud setup and deployment tools, minimizing the layers of lock-in between an organization and a cloud provider.

  • Complementing other cloud security tools: Create a first line of defense for cloud deployment errors, reducing the volume of alerts for customers also using CSPM tools or Cloud Native Application Protection Platforms (CNAPPs). 


An intelligent proxy that uses a policy-based approach to
enforce secure configuration of cloud resources.

Better together with Cloudflare One

As a SASE platform, Cloudflare One ensures safe access and provides data controls for cloud and SaaS apps. This integration broadens the scope of Cloudflare’s SASE platform beyond user-facing applications to incorporate increased cloud security through proactive configuration management of infrastructure services, beyond what CSPM and CASB solutions provide. With the addition of Kivera to Cloudflare One, customers now have a unified platform for all their inline protections, including cloud control, access management, and threat and data protection. All of these features are available with single-pass inspection, which is 50% faster than Secure Web Gateway (SWG) alternatives.  

With the earlier acquisition of BastionZero, a Zero Trust infrastructure access company, Cloudflare One expanded the scope of its VPN replacement solution to cover infrastructure resources as easily as it does apps and networks. Together Kivera and BastionZero enable centralized security management across hybrid IT environments, and provide a modern DevOps-friendly way to help enterprises connect and protect their hybrid infrastructure with Zero Trust best practices.

Beyond its SASE capabilities, Cloudflare One is integral to Cloudflare’s connectivity cloud, enabling organizations to consolidate IT security tools on a single platform. This simplifies secure access to resources, from developer privileged access to technical infrastructure and expanding cloud services. As Forrester echoes, “Cloudflare is a good choice for enterprise prospects seeking a high-performance, low-maintenance, DevOps-oriented solution.”

The growing threat of cloud misconfigurations

The cloud has become a prime target for cyberattacks. According to the 2023 Cloud Risk Report, CrowdStrike observed a 95% increase in cloud exploitation from 2021 to 2022, with a staggering 288% jump in cases involving threat actors directly targeting the cloud.

Misconfigurations in cloud infrastructure settings, such as improperly set security parameters and default access controls, provide adversaries with an easy path to infiltrate the cloud. According to the 2023 Thales Global Cloud Security Study, which surveyed nearly 3,000 IT and security professionals from 18 countries, 44% of respondents reported experiencing a data breach, with misconfigurations and human error identified as the leading cause, accounting for 31% of the incidents.

Further, according to GartnerⓇ, “Through 2027, 99% of records compromised in cloud environments will be the result of user misconfigurations and account compromise, not the result of an issue with the cloud provider.”1

Several factors contribute to the rise of cloud misconfigurations:

  • Rapid adoption of cloud services: Leaders are often driven by the scalability, cost-efficiency, and ability to support remote work and real-time collaboration that cloud services offer. These factors enable rapid adoption of cloud services which can lead to unintentional misconfigurations as IT teams struggle to keep up with the pace and complexity of these services. 

  • Complexity of cloud environments: Cloud infrastructure can be highly complex with multiple services and configurations to manage. For example, AWS alone offers 373 services with 15,617 actions and 140,000+ parameters, making it challenging for IT teams to manage settings accurately. 

  • Decentralized management: In large organizations, cloud infrastructure resources are often managed by multiple teams or departments. Without centralized oversight, inconsistent security policies and configurations can arise, increasing the risk of misconfigurations.

  • Continuous Integration and Continuous Deployment (CI/CD): CI/CD pipelines promote the ability to rapidly deploy, change and frequently update infrastructure. With this velocity comes the increased risk of misconfigurations when changes are not properly managed and reviewed.

  • Insufficient training and awareness: Employees may lack the cross-functional skills needed for cloud security, such as understanding networks, identity, and service configurations. This knowledge gap can lead to mistakes and increases the risk of misconfigurations that compromise security.

Common exploitation methods 

Threat actors exploit cloud services through various means, including targeting misconfigurations, abusing privileges, and bypassing encryption. Misconfigurations such as exposed storage buckets or improperly secured APIs offer attackers easy access to sensitive data and resources. Privilege abuse occurs when attackers gain unauthorized access through compromised credentials or poorly managed identity and access management (IAM) policies, allowing them to escalate their access and move laterally within the cloud environment. Additionally, unencrypted data enables attackers to intercept and decrypt data in transit or at rest, further compromising the integrity and confidentiality of sensitive information.

Here are some other vulnerabilities that organizations should address: 

  • Unrestricted access to cloud tenants: Allowing unrestricted access exposes cloud platforms to data exfiltration by malicious actors. Limiting access to approved tenants with specific IP addresses and service destinations helps prevent unauthorized access.

  • Exposed access keys: Exposed access keys can be exploited by unauthorized parties to steal or delete data. Requiring encryption for the access keys and restricting their usage can mitigate this risk.

  • Excessive account permissions: Granting excessive privileges to cloud accounts increases the potential impact of security breaches. Limiting permissions to necessary operations helps prevent lateral movement and privilege escalation by threat actors.

  • Inadequate network segmentation: Poorly managed network security groups and insufficient segmentation practices can allow attackers to move freely within cloud environments. Drawing boundaries around your cloud resources and tenants ensures that data stays within your organization.

  • Improper public access configuration: Incorrectly exposing critical services or storage resources to the internet increases the likelihood of unauthorized access and data compromise. Preventing public access drastically reduces risk.

  • Shadow cloud infrastructure: Abandoned or neglected cloud instances are often left vulnerable to exploitation, providing attackers with opportunities to access sensitive data left behind. Preventing untagged or unapproved cloud resources to be created can reduce the risk of exposure.

Limitations of existing tools 

Many organizations turn to CSPM tools to give them more visibility into cloud misconfigurations. These tools often alert teams after an issue occurs, putting security teams in a reactive mode. Remediation efforts require collaboration between security teams and developers to implement changes, which can be time-consuming and resource-intensive. This approach not only delays issue resolution but also exposes companies to compliance and legal risks, while failing to train employees on secure cloud practices. On average, it takes 207 days to identify these breaches and an additional 70 days to contain them. 

Addressing the growing threat of cloud misconfigurations requires proactive security measures and continuous monitoring. Organizations must adopt proactive security solutions that not only detect and alert but also prevent misconfigurations from occuring in the first place and enforce best practices. Creating a first line of defense for cloud deployment errors reduces the volume of alerts for customers, especially those also using CSPM tools or CNAPPs. 

By implementing these proactive strategies, organizations can safeguard their cloud environments against the evolving landscape of cyber threats, ensuring robust security and compliance while minimizing risks and operational disruptions.

What’s next for Kivera

The Kivera product will not be a point solution add-on. We’re making it a core part of our Cloudflare One offering because integrating features from products like our Secure Web Gateway give customers a comprehensive solution that works better together.

We’re excited to welcome Kivera to the Cloudflare team. Through the end of 2024 and into early 2025, Kivera’s team will focus on integrating their preventive inline cloud app controls directly into Cloudflare One. We are looking for early access testers and teams to provide feedback about what they would like to see. If you’d like early access, please join the waitlist.

[1] Source: Outcome-Driven Metrics You Can Use to Evaluate Cloud Security Controls, Gartner, Charlie Winckless, Paul Proctor, Manuel Acosta, 09/28/2023 

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Announcing two highly requested DLP enhancements: Optical Character Recognition (OCR) and Source Code Detections

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/dlp-ocr-sourcecode


We are excited to announce two enhancements to Cloudflare’s Data Loss Prevention (DLP) service: support for Optical Character Recognition (OCR) and predefined source code detections. These two highly requested DLP features make it easier for organizations to protect their sensitive data with granularity and reduce the risks of breaches, regulatory non-compliance, and reputational damage:

  • With OCR, customers can efficiently identify and classify sensitive information contained within images or scanned documents.
  • With predefined source code detections, organizations can scan inline traffic for common code languages and block those HTTP requests to prevent data leaks, as well as detecting the storage of code in repositories such as Google Drive.

These capabilities are available now within our DLP engine, which is just one of several Cloudflare services, including cloud access security broker (CASB), Zero Trust network access (ZTNA), secure web gateway (SWG), remote browser isolation (RBI), and cloud email security, that help organizations protect data everywhere across web, SaaS, and private applications.

About Optical Character Recognition (OCR)

OCR enables the extraction of text from images. It converts the text within those images into readable text data that can be easily edited, searched, or analyzed, unlike images.

Sensitive data regularly appears in image files. For example, employees are often asked to provide images of identification cards, passports, or documents as proof of identity or work status. Those images can contain a plethora of sensitive and regulated classes of data, including Personally Identifiable Information (PII) — for example, passport numbers, driver’s license numbers, birthdates, tax identification numbers, and much more.

OCR can be leveraged within DLP policies to prevent the unauthorized sharing or leakage of sensitive information contained within images. Policies can detect when sensitive text content is being uploaded to cloud storage or shared through other communication channels, and block the transaction to prevent data loss. This assists in enforcing compliance with regulatory requirements related to data protection and privacy.

About source code detection

Source code fuels digital business and contains high-value intellectual property, including proprietary algorithms and encrypted secrets about a company’s infrastructure. Source code has been and will continue to be a target for theft by external attackers, but customers are also increasingly concerned about the inadvertent exposure of this information by internal users. For example, developers may accidentally upload source code to a publicly available GitHub repository or to generative AI tools like ChatGPT. While these tools have their place (like using AI to help with debugging), security teams want greater visibility and more precise control over what data flows to and from these tools.

To help customers, Cloudflare now offers predefined DLP profiles for common code languages — specifically C, C++, C#, Go, Haskell, Java, Javascript, Lua, Python, R, Rust, and Swift. These machine learning-based detections train on public repositories for algorithm development, ensuring they remain up to date. Cloudflare’s DLP inspects the HTTP body of requests for these DLP profiles, and security teams can block traffic accordingly to prevent data leaks.

How to use these capabilities

Cloudflare offers you flexibility to determine what data you are interested in detecting via DLP policies. You can use predefined profiles created by Cloudflare for common types of sensitive or regulated data (e.g. credentials, financial data, health data, identifiers), or you can create your own custom detections.

To implement inline blocking of source code, simply select the DLP profiles for the languages you want to detect. For example, if my organization uses Rust, Go, and JavaScript, I would turn on those detections:

I would then create a blocking policy via our secure web gateway to prevent traffic containing source code. Here, we block source code from being uploaded to ChatGPT:

Adding OCR to any detection is similarly easy. Below is a profile looking for sensitive data that could be stored in scanned documents.

With the detections selected, simply enable the OCR toggle, and wherever you are applying DLP inspections, images in your content will be scanned for sensitive data. The detections work the same in images as they do in the text, including Match Counts and Context Analysis, so no additional logic or settings are needed.

Consistency across use cases is a core principle of our DLP solution, so as always, this feature is available for both data at rest, available via CASB, and data in transit, available via Gateway.

How do I get started?

DLP is available with other data protection services as part of Cloudflare One, our Secure Access Service Edge (SASE) platform that converges Zero Trust security and network connectivity services. To get started protecting your sensitive data, reach out for a consultation, or contact your account manager.

Introducing behavior-based user risk scoring in Cloudflare One

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/cf1-user-risk-score


Cloudflare One, our secure access service edge (SASE) platform, is introducing new capabilities to detect risk based on user behavior so that you can improve security posture across your organization.

Traditionally, security and IT teams spend a lot of time, labor, and money analyzing log data to track how risk is changing within their business and to stay on top of threats. Sifting through such large volumes of data – the majority of which may well be benign user activity – can feel like finding a needle in a haystack.

Cloudflare’s approach simplifies this process with user risk scoring. With AI/machine learning techniques, we analyze the real-time telemetry of user activities and behaviors that pass through our network to identify abnormal behavior and potential indicators of compromises that could lead to danger for your organization, so your security teams can lock down suspicious activity and adapt your security posture in the face of changing risk factors and sophisticated threats.

User risk scoring

The concept of trust in cybersecurity has evolved dramatically. The old model of “trust but verify” has given way to a Zero Trust approach, where trust is never assumed and verification is continuous, as each network request is scrutinized. This form of continuous evaluation enables administrators to grant access based not just on the contents of a request and its metadata, but on its context — such as whether the user typically logs in at that time or location.

Previously, this kind of contextual risk assessment was time-consuming and required expertise to parse through log data. Now, we’re excited to introduce Zero Trust user risk scoring which does this automatically, allowing administrators to specify behavioral rules — like monitoring for anomalous “impossible travel” and custom Data Loss Prevention (DLP) triggers, and use these to generate dynamic user risk scores.

Zero Trust user risk scoring detects user activity and behaviors that could introduce risk to your organizations, systems, and data and assigns a score of Low, Medium, or High to the user involved. This approach is sometimes referred to as user and entity behavior analytics (UEBA) and enables teams to detect and remediate possible account compromise, company policy violations, and other risky activity.

How risk scoring works and detecting user risk

User risk scoring is built to examine behaviors. Behaviors are actions taken or completed by a user and observed by Cloudflare One, our SASE platform that helps organizations implement Zero Trust.

Once tracking for a particular behavior is enabled, the Zero Trust risk scoring engine immediately starts to review existing logs generated within your Zero Trust account. Then, after a user in your account performs a behavior that matches one of the enabled risk behaviors based on observed log data, Cloudflare assigns a risk score — Low, Medium, or High — to the user who performed the behavior.

Behaviors are built using log data from within your Cloudflare account. No additional user data is being collected, tracked or stored beyond what is already available in the existing Zero Trust logs (which adhere to the log retention timeframes).

A popular priority amongst security and insider threat teams is detecting when a user performs so-called “impossible travel”. Impossible travel, available as a predefined risk behavior today, is when a user completes a login from two different locations that the user could not have traveled to in that period of time. For example, if Alice is in Seattle and logs into her organization’s finance application that is protected by Cloudflare Access and only a few minutes later is seen logging into her organization’s business suite from Sydney, Australia, impossible travel would be triggered and Alice would be assigned a risk level of High.

For users that are observed performing multiple risk behaviors, they will be assigned the highest-level risk behavior they’ve triggered. This real-time risk assessment empowers your security teams to act swiftly and decisively.

Zero Trust user risk scoring detecting impossible travel and flagging a user as high risk

Enabling predefined risk behaviors

Behaviors can be enabled and disabled at any time, but are disabled by default. Therefore, users will not be assigned risk scores until you have decided what is considered a risk to your organization and how urgent that risk is.

To start detecting a given risk behavior, an administrator must first ensure the behavior requirements are met (for instance, to detect whether a user has triggered a high number of DLP policies, you’ll need to first set up a DLP profile). From there, simply enable the behavior in the Zero Trust dashboard.

After a behavior has been enabled, Cloudflare will start analyzing behaviors to flag users with the corresponding risk when detected. The risk level of any behavior can be changed by an administrator. You have the freedom to enable behaviors that are relevant to your security posture as well as adjust the default risk score (Low, Medium, or High) from an out-of-the-box assignment.

And for security administrators who have investigated a user and need to clear a user’s risk score, simply go to Risk score > User risk scoring, choose the appropriate user, and select ‘Reset user risk’ followed by ‘Confirm.’ Once a user’s risk score is reset, they disappear from the risk table — until or unless they trigger another risk behavior.

Zero Trust user risk scoring behaviors can be enabled in seconds

How do I get started?

User risk scoring and DLP are part of Cloudflare One, which converges Zero Trust security and network connectivity services on one unified platform and global control plane.

To get access via Cloudflare One, reach out for a consultation, or contact your account manager.

DLP Exact Data Match beta now available

Post Syndicated from Noelle Kagan original http://blog.cloudflare.com/edm-beta/

DLP Exact Data Match beta now available

DLP Exact Data Match beta now available

The most famous data breaches–the ones that keep security practitioners up at night–involved the leak of millions of user records. Companies have lost names, addresses, email addresses, Social Security numbers, passwords, and a wealth of other sensitive information. Protecting this data is the highest priority of most security teams, yet many teams still struggle to actually detect these leaks.

Cloudflare’s Data Loss Prevention suite already includes the ability to identify sensitive data like credit card numbers, but with the volume of data being transferred every day, it can be challenging to understand which of the transactions that include sensitive data are actually problematic. We hear customers tell us, “I don’t care when one of my employees uses a personal credit card to buy something online. Tell me when one of my customers’ credit cards are leaked.”

In response, we looked for a method to distinguish between any credit card and one belonging to a specific customer. We are excited to announce the launch of our newest Data Loss Prevention feature, Exact Data Match. With Exact Data Match (EDM), customers securely tell us what data they want to protect, and then we identify, log, and block the presence or movement of that data. For example, if you provide us with a set of credit card numbers, we will DLP scan your traffic or repositories for only those cards. This allows you to create targeted DLP detections for your organization.

What is Exact Data Match?

Many Data Loss Prevention (DLP) detections begin with a generic identification of a pattern, often using a regular expression, and then are validated by additional criteria. Validation can leverage a wide range of techniques from checksums to machine learning models. However, this validates that the pattern is a credit card, not that it is your credit card.

With Exact Data Match, you tell us exactly the data you want to protect, but we never see it in cleartext. You provide a list of data of your choosing, such as a list of names, addresses, or credit card numbers, and that data is hashed before ever reaching Cloudflare. We store the hashes and scan your traffic or content for matches of the hashes. When we find a match, we log or block it according to your policy.

By using a finite list of data, we drastically reduce false positives compared to generic pattern matching. Meanwhile, hashing the data maintains your data privacy. Our goal is to meet your data protection and privacy needs.

How do I use it?

We now offer you the ability to upload DLP datasets. These allow you to provide batches of data to be used for your DLP detections.

DLP Exact Data Match beta now available

When creating a dataset, provide a name, description, and a file containing the data to match.

DLP Exact Data Match beta now available

When you upload the file, Cloudflare one-way hashes the data right in your browser. The hashed data is then transferred via API to Cloudflare, while the cleartext data never leaves the browser.

You can see the status of the upload in the datasets table.

DLP Exact Data Match beta now available

The dataset can now be added to a DLP profile for detection. You can also add other predefined and custom entries to the same DLP profile.

DLP Exact Data Match beta now available

DLP Profiles can be used for inline scanning and protection with Cloudflare Gateway or scanning your data at rest with Cloudflare CASB.

Can I join the beta?

Exact data match is now available for every DLP customer. If you are not a DLP customer but would like to learn more about Cloudflare One and DLP, reach out for a consultation.

What’s next?

Customers have many different formats to store data, and many different ways in which they want to monitor it. Our goal is to offer as much flexibility as your organization needs to meet your data protection goals.

Cloudflare One DLP integrates with Microsoft Information Protection labels

Post Syndicated from Noelle Kagan original https://blog.cloudflare.com/cloudflare-dlp-mip/

Cloudflare One DLP integrates with Microsoft Information Protection labels

Cloudflare One DLP integrates with Microsoft Information Protection labels

The crown jewels for an organization are often data, and the first step in protection should be locating where the most critical information lives. Yet, maintaining a thorough inventory of sensitive data is harder than it seems and generally a massive lift for security teams. To help overcome data security troubles, Microsoft offers their customers data classification and protection tools. One popular option are the sensitivity labels available with Microsoft Purview Information Protection. However, customers need the ability to track sensitive data movement even as it migrates beyond the visibility of Microsoft.

Today, we are excited to announce that Cloudflare One now offers Data Loss Prevention (DLP) detections for Microsoft Purview Information Protection labels. Simply integrate with your Microsoft account, retrieve your labels, and build rules to guide the movement of your labeled data. This extends the power of Microsoft’s labels to any of your corporate traffic in just a few clicks.

Data Classification with Microsoft Labels

Every organization has a wealth of data to manage, from publicly accessible data, like documentation, to internal data, like the launch date of a new product. Then, of course, there is the data requiring the highest levels of protection, such as customer PII. Organizations are responsible for confining data to the proper destinations while still supporting accessibility and productivity, which is no small feat.

Microsoft Purview Information Protection offers sensitivity labels to let you classify your organization’s data. With these labels, Microsoft provides the ability to protect sensitive data, while still enabling productivity and collaboration. Sensitivity labels can be used in a number of Microsoft applications, which includes the ability to apply the labels to Microsoft Office documents. The labels correspond to the sensitivity of the data within the file, such as Public, Confidential, or Highly Confidential.

Cloudflare One DLP integrates with Microsoft Information Protection labels

The labels are embedded in a document’s metadata and are preserved even when it leaves the Microsoft environment, such as a download from OneDrive.

Sync Cloudflare One and Microsoft Information Protection

Cloudflare One, our SASE platform that delivers network-as-a-service (NaaS) with Zero Trust security natively built-in, connects users to enterprise resources, and offers a wide variety of opportunities to secure corporate traffic, including the inspection of data moving across the Microsoft productivity suite. We’ve designed Cloudflare One to act as a single pane of glass for your organization. This means that after you’ve deployed any of our Zero Trust services, whether that be Zero Trust Network Access or Secure Web Gateway, you are clicks, not months, away from deploying Data Loss Prevention, Cloud Access Security Broker, Email Security, and Browser Isolation to enhance your Microsoft security and overall data protection.

Specifically, Cloudflare’s API-driven Cloud Access Security Broker (CASB) can scan SaaS applications like Microsoft 365 for misconfigurations, unauthorized user activity, shadow IT, and other data security issues that can occur after a user has successfully logged in.

With this new integration, CASB can now also retrieve Information Protection labels from your Microsoft account. If you have labels configured, upon integration, CASB will automatically populate the labels into a Data Loss Prevention profile.

Cloudflare One DLP integrates with Microsoft Information Protection labels

DLP profiles are the building blocks for applying DLP scanning. They are where you identify the sensitive data you want to protect, such as Microsoft labeled data, credit card numbers, or custom keywords. Your labels are stored as entries within the Microsoft Purview Information Protection Sensitivity Labels profile using the name of your CASB integration. You can also add the labels to custom DLP profiles, of  fering more detection flexibility.

Build DLP Rules

You can now extend the power of Microsoft’s labels to protect your data as it moves to other platforms. By building DLP rules, you determine how labeled data can move around and out of your corporate network. Perhaps you don’t want to allow Highly Confidential labels to be downloaded from your OneDrive account, or you don’t want any data more sensitive than Confidential to be uploaded to file sharing sites that you don’t use. All of this can be implemented using DLP and Cloudflare Gateway.

Simply navigate to your Gateway Firewall Policies and start implementing building rules using your DLP profiles:

Cloudflare One DLP integrates with Microsoft Information Protection labels

How to Get Started

To get access to DLP, reach out for a consultation, or contact your account manager.