Tag Archives: google

Detecting Malicious Trackers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/05/detecting-malicious-trackers.html

From Slashdot:

Apple and Google have launched a new industry standard called “Detecting Unwanted Location Trackers” to combat the misuse of Bluetooth trackers for stalking. Starting Monday, iPhone and Android users will receive alerts when an unknown Bluetooth device is detected moving with them. The move comes after numerous cases of trackers like Apple’s AirTags being used for malicious purposes.

Several Bluetooth tag companies have committed to making their future products compatible with the new standard. Apple and Google said they will continue collaborating with the Internet Engineering Task Force to further develop this technology and address the issue of unwanted tracking.

This seems like a good idea, but I worry about false alarms. If I am walking with a friend, will it alert if they have a Bluetooth tracking device in their pocket?

Another Chrome Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/05/another-chrome-vulnerability.html

Google has patched another Chrome zero-day:

On Thursday, Google said an anonymous source notified it of the vulnerability. The vulnerability carries a severity rating of 8.8 out of 10. In response, Google said, it would be releasing versions 124.0.6367.201/.202 for macOS and Windows and 124.0.6367.201 for Linux in subsequent days.

“Google is aware that an exploit for CVE-2024-4671 exists in the wild,” the company said.

Google didn’t provide any other details about the exploit, such as what platforms were targeted, who was behind the exploit, or what they were using it for.

Class-Action Lawsuit against Google’s Incognito Mode

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/04/class-action-lawsuit-against-googles-incognito-mode.html

The lawsuit has been settled:

Google has agreed to delete “billions of data records” the company collected while users browsed the web using Incognito mode, according to documents filed in federal court in San Francisco on Monday. The agreement, part of a settlement in a class action lawsuit filed in 2020, caps off years of disclosures about Google’s practices that shed light on how much data the tech giant siphons from its users­—even when they’re in private-browsing mode.

Under the terms of the settlement, Google must further update the Incognito mode “splash page” that appears anytime you open an Incognito mode Chrome window after previously updating it in January. The Incognito splash page will explicitly state that Google collects data from third-party websites “regardless of which browsing or browser mode you use,” and stipulate that “third-party sites and apps that integrate our services may still share information with Google,” among other changes. Details about Google’s private-browsing data collection must also appear in the company’s privacy policy.

I was an expert witness for the prosecution (that’s the class, against Google). I don’t know if my declarations and deposition will become public.

Google Pays $10M in Bug Bounties in 2023

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/google-pays-10m-in-bug-bounties-in-2023.html

BleepingComputer has the details. It’s $2M less than in 2022, but it’s still a lot.

The highest reward for a vulnerability report in 2023 was $113,337, while the total tally since the program’s launch in 2010 has reached $59 million.

For Android, the world’s most popular and widely used mobile operating system, the program awarded over $3.4 million.

Google also increased the maximum reward amount for critical vulnerabilities concerning Android to $15,000, driving increased community reports.

During security conferences like ESCAL8 and hardwea.io, Google awarded $70,000 for 20 critical discoveries in Wear OS and Android Automotive OS and another $116,000 for 50 reports concerning issues in Nest, Fitbit, and Wearables.

Google’s other big software project, the Chrome browser, was the subject of 359 security bug reports that paid out a total of $2.1 million.

Slashdot thread.

AI and the Evolution of Social Media

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/ai-and-the-evolution-of-social-media.html

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracy, malfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favorable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviors.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporations (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal gray area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behavior seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.

This essay was written with Nathan Sanders, and was originally published in MIT Technology Review.

New Image/Video Prompt Injection Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/new-image-video-prompt-injection-attacks.html

Simon Willison has been playing with the video processing capabilities of the new Gemini Pro 1.5 model from Google, and it’s really impressive.

Which means a lot of scary new video prompt injection attacks. And remember, given the current state of technology, prompt injection attacks are impossible to prevent in general.

NVIDIA Shows Intel Gaudi2 is 4x Better Performance Per Dollar than its H100

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/nvidia-shows-intel-gaudi2-is-4x-better-performance-per-dollar-than-its-h100/

In a stunning twist, NVIDIA shows the Intel Gaudi2 is roughly 4x better performance per dollar than the H100 in its MLPerf Training results

The post NVIDIA Shows Intel Gaudi2 is 4x Better Performance Per Dollar than its H100 appeared first on ServeTheHome.

MLPerf Inference v3.1 Shows NVIDIA Grace Hopper and a Cool AMD TPU v5e Win

Post Syndicated from Cliff Robinson original https://www.servethehome.com/mlperf-inference-v3-1-shows-nvidia-grace-hopper-and-a-cool-amd-tpu-v5e-win/

NVIDIA’s MLPerf Inference v3.1 is out. Two standouts were NVIDIA setting the stage to jettison x86 and AMD having a big win at Google

The post MLPerf Inference v3.1 Shows NVIDIA Grace Hopper and a Cool AMD TPU v5e Win appeared first on ServeTheHome.

Google Details TPUv4 and its Crazy Optically Reconfigurable AI Network

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/google-details-tpuv4-and-its-crazy-optically-reconfigurable-ai-network/

Google detailed how its TPUv4 pods use optically reconfigurable networks to support efficient, large scale, AI workloads

The post Google Details TPUv4 and its Crazy Optically Reconfigurable AI Network appeared first on ServeTheHome.

Google Reportedly Disconnecting Employees from the Internet

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/google-reportedly-disconnecting-employees-from-the-internet.html

Supposedly Google is starting a pilot program of disabling Internet connectivity from employee computers:

The company will disable internet access on the select desktops, with the exception of internal web-based tools and Google-owned websites like Google Drive and Gmail. Some workers who need the internet to do their job will get exceptions, the company stated in materials.

Google has not confirmed this story.

More news articles.

Google Is Using Its Vast Data Stores to Train AI

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/google-is-using-its-vast-data-stores-to-train-ai.html

No surprise, but Google just changed its privacy policy to reflect broader uses of all the surveillance data it has captured over the years:

Research and development: Google uses information to improve our services and to develop new products, features and technologies that benefit our users and the public. For example, we use publicly available information to help train Google’s AI models and build products and features like Google Translate, Bard, and Cloud AI capabilities.

(I quote the privacy policy as of today. The Mastodon link quotes the privacy policy from ten days ago. So things are changing fast.)

Google Is Not Deleting Old YouTube Videos

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/05/google-is-not-deleting-old-youtube-videos.html

Google has backtracked on its plan to delete inactive YouTube videos—at least for now. Of course, it could change its mind anytime it wants.

It would be nice if this would get people to think about the vulnerabilities inherent in letting a for-profit monopoly decide what of human creativity is worth saving.

Malware Delivered through Google Search

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/malware-delivered-through-google-search.html

Criminals using Google search ads to deliver malware isn’t new, but Ars Technica declared that the problem has become much worse recently.

The surge is coming from numerous malware families, including AuroraStealer, IcedID, Meta Stealer, RedLine Stealer, Vidar, Formbook, and XLoader. In the past, these families typically relied on phishing and malicious spam that attached Microsoft Word documents with booby-trapped macros. Over the past month, Google Ads has become the go-to place for criminals to spread their malicious wares that are disguised as legitimate downloads by impersonating brands such as Adobe Reader, Gimp, Microsoft Teams, OBS, Slack, Tor, and Thunderbird.


It’s clear that despite all the progress Google has made filtering malicious sites out of returned ads and search results over the past couple decades, criminals have found ways to strike back. These criminals excel at finding the latest techniques to counter the filtering. As soon as Google devises a way to block them, the criminals figure out new ways to circumvent those protections.

Real-Time Risk Mitigation in Google Cloud Platform

Post Syndicated from Ben Austin original https://blog.rapid7.com/2022/10/12/real-time-risk-mitigation-in-google-cloud-platform/

Real-Time Risk Mitigation in Google Cloud Platform

With Google Cloud Next happening this week, there’s been some recent water cooler talk – okay, informal, ad hoc Zoom calls – where discussions about what makes Google Cloud Platform (GCP) unique when it comes to security. A few specific differences have popped up here and there (default data encryption, the way IAM is handled, etc.), but, generally speaking, many of the principles that apply to all other cloud providers apply to GCP environments.

For one, due to the speed and scale of these environments, it’s simultaneously very difficult and extremely critical to maintain an up-to-date inventory of the state of all resources in your environment. This means constantly monitoring your environment for resources being created, deleted, or modified in as close to real time as possible.

And in an effort to avoid ambiguity or hide behind marketing buzz terms, when I’m referring to “real time” here, I’m talking about sub 5-minute intervals based on activity happening in the environment. This is not to be confused with “near real time” approaches some vendors tout, which, in reality, still only pulls in data once or twice a day based on a static schedule.

In GCP, like in AWS, Azure, and all other cloud environments, simply getting a snapshot once a day to identify misconfigurations, vulnerabilities, or suspicious behaviors like you might with an on-prem data center just isn’t a scalable strategy. It’s a common cliche, but the ephemeral nature and rate of change in public cloud environments makes that kind of scanning strategy extremely ineffective when it comes to monitoring, analyzing, and eliminating actual risk in a cloud environment.

Let me lay out a couple examples where this kind of real-time monitoring can provide significant, potentially necessary, value to security teams working to make their cloud risk management programs more effective.

Identification of high-risk resources

As an example, say a developer is in a GCP project associated with your company’s revenue-generating application and they spin up a Cloud Storage instance that is, whether mistakenly or maliciously, open to the public internet.

If your security team is reliant on a scan to happen 12 hours later to get visibility into this activity, your organization will constantly be left open to significant risk. Take away the hyperbole here and assume it’s a much smaller risk or compliance violation. Even in that situation, your team is still working from behind and, presumably, almost always facing some level of stress about what issues are out there in the environment that they won’t know about for another 12-18 hours.

Worst of all, with this type of scanning you’re generally just getting a point-in-time snapshot of the environment and usually don’t know who made the change or how long ago it happened. This makes it much more difficult and time consuming for your team to actually assess the risk or get their hands on the right information to make an informed decision about how the situation should be addressed.

When a team is working with real-time data, however, they can be much more diligent and confident that they’re prioritizing the right issues at any given moment, with all the necessary context about who made the change and when it occurred. This not only helps teams stay ahead of issues and reduce the risk of a breach in their environment, but also helps keep individuals and teams feeling positive about the impact that the program is having on the organization.

Delayed remediation workflows

Building off of the previous example, it’s not only that teams can’t respond to risk they haven’t been notified of, it’s also that any automated response workflows your team may have built out to be more efficient are significantly less effective when they’re triggered by hours-old data. A 12-hour delay in an automation workflow all but eliminates the value of the automation itself, and it can actually cause headaches and confusion that detract from your team’s efficiency, rather than improving it (more on this in the next example).

In contrast, if you’re able to detect risky changes to your environment as they happen, you can automatically respond to that issue as it happens. In the case of this all being a mistake caused by a developer working a little too quickly, you’re able to automatically notify them of their error within a matter of minutes, likely while they’re still working within that project. Giving your development team this kind of feedback in the moment, rather than forcing them to context switch and go back into the project to fix the error a day later, is an excellent way to build stronger relationships and rapport with that team.

In the more rare case that this is indeed a malicious internal or external actor, enabling your automated remediation workflows to kick into gear within seconds and potentially stop the behavior could mean the difference between a minor incident and a breach requiring public disclosure from your organization.

Minimizing false positives and cross-team friction

Speaking of relationships with the development team (sorry, #DevSecOps), I can almost guarantee that working with data from scans or snapshots that occur every 12 or 24 hours in your cloud will cause friction between your two teams. Whether it’s tied to manual identification of risky resources or automated workflows notifying them of a non-compliant asset, working with stale data will inevitably lead to false positives that will both annoy and distract your already overburdened development team.

Take the example highlighted above, but instead, let’s say the developer actually spun up that Cloud Storage instance for a short amount of time in a dev instance with no actual customer data as part of a testing exercise. By the time your team gets visibility into this and either reaches out manually or has some automated notification sent to the developer, that instance could have already been deleted for hours. Now your team is looking at one set of old data and seeing an issue, meanwhile the developer is insisting that the storage container doesn’t even exist anymore. As mentioned above, this is going to cause headaches and frustration for both parties, and cause your team to lose credibility with the dev team.

At this point, you can probably guess where this is going next. With real-time monitoring in your environment this situation can be avoided altogether because your team will be looking at the same up-to-date information, and your team will be able to see that the storage container was shut down or removed from the project rather than spending time chasing down a false positive.

Earlier this month we released event-driven harvesting for GCP in InsightCloudSec. This agentless, real-time monitoring helps your security team achieve every one of the benefits outlined above while also avoiding API rate limiting. In addition, we’ve recently added GCP CIS Benchmarks v1.3.0, added GCP threat findings into our console, and added support for Google Directory to give visibility into IAM factors such as user last login, MFA status, group association and more.

If you want to learn more about how Rapid7 can help you secure Google Cloud Platform, or any other public cloud environment, sign up for our live bi-weekly demo of InsightCloudSec.