Milliseconds to breach: How patch automation closes attackers’ fastest loophole

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Milliseconds to breach: How patch automation closes attackers’ fastest loophole

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here. Procrastinating about patching has killed more networks and damaged more companies than any zero-day exploit or advanced cyberattack. Complacency kills — and carries a high price. Down-rev (having old patches in place that are “down revision”) or no patching at all is how ransomware gets installed, data breaches occur and companies are fined for being out of compliance. It isn’t a matter of if a company will be breached but when — particularly if they don’t prioritize patch management. Why so many security teams procrastinate – and pay a high price Let’s be honest about how patching is perceived in many security teams and across IT organizations: It’s often delegated to staff members assigned with the department’s most rote, mundane tasks. Why? No one wants to spend their time on something that is often repetitive and at times manually intensive, yet requires complete focus to get done right. Most security and IT teams tell VentureBeat in confidence that patching is too time-consuming and takes away from more interesting projects. That’s consistent with an Ivanti study that found that the majority (71%) of IT and security professionals think patching is overly complex, cumbersome and time-consuming. Remote work and decentralized workspaces make patching even more complicated, 57% of security professionals reported. Also consistent with what VentureBeat is hearing from security teams, Ivanti found that 62% of IT and security leaders admit that patch management takes a backseat to other tasks. The truth is that device inventory and manual approaches to patch management haven’t been keeping up for a while (years). In the meantime, adversaries are busy improving their tradecraft, creating weaponized large language models (LLMs) and attack apps. Not patching? It’s like taking the lock off your front door Crime waves are hitting affluent, gated communities as criminals use remote video cameras for 24/7 surveillance. Leaving a home unlocked without a security system is an open invitation for robbers. Not patching endpoints is the same. And, let’s be honest: Any task that gets deprioritized and pushed down action item lists will most likely never be entirely completed. Adversaries are improving their tradecrafts all the time by studying common vulnerabilities and exposures (CVEs) and finding lists of companies that have those vulnerabilities — making them even more susceptible targets. Gartner often weighs in on patching in their research and considers it part of their vulnerability management coverage. Their recent study, Top 5 Elements of Effective Vulnerability Management, emphasizes that “many organizations still mismanage patching exceptions, resulting in missing or ineffective mitigations and increased risk.” Mismanagement starts when teams deprioritize patching and consider manual processes “good enough” to complete increasingly complex, challenging and mundane tasks. This is made worse with siloed teams. Such mismanagement creates exploitable gaps. The old mantra “scan, patch, rescan” isn’t scaling when adversaries are using AI and generative AI attacks to scan for endpoints to target at machine speed. GigaOm’s Radar for Unified Endpoint Management (UEM) report further highlights how patching remains a significant challenge, with many vendors struggling to provide consistent application, device driver and firmware patching. The report urges organizations to consider how they can improve patch management as part of a broader effort to automate and scale vulnerability management. Why traditional patch management fails in today’s threat landscape Patch management in most organizations begins with scheduled monthly cycles that rely on static Common Vulnerability Scoring System (CVSS) severity scores to help prioritize vulnerabilities. Adversaries are moving faster and creating more complex threats than CVSS scores can keep up with. As Karl Triebes, Ivanti’s CPO, explained: “Relying solely on severity ratings and a fixed monthly cycle exposes organizations to unaccounted risk. These ratings overlook unique business context, security gaps and evolving threats.” In today’s fast-moving environment, static scores cannot capture an organization’s nuanced risk profile. Gartner’s framework underscores the need for “advanced prioritization techniques and automated workflows that integrate asset criticality and active threat data to direct limited resources toward vulnerabilities that truly matter.” The GigaOm report similarly notes that, while most UEM solutions support OS patching, fewer provide “patching for third-party applications, device drivers and firmware,” leaving gaps that adversaries exploit. Risk-based and continuous patch management: A smarter approach Chris Goettl, Ivanti’s VP of product management for endpoint security, explained to VentureBeat: “Risk-based patch prioritization goes beyond CVSS scores by considering active exploitation, threat intelligence and asset criticality.” Taking this more dynamic approach helps organizations anticipate and react to risks in real time, which is far more efficient than using CVSS scores. Triebes expanded: “Relying solely on severity ratings and a fixed monthly cycle exposes organizations to unaccounted risk. These ratings overlook your unique business context, security gaps and evolving threats.” However, prioritization alone isn’t enough. Adversaries can quickly weaponize vulnerabilities within hours and have proven that genAI is making them even more efficient than in the past. Ransomware attackers find new ways to weaponize old vulnerabilities. Organizations following monthly or quarterly patching cycles can’t keep up with the pace of new tradecraft. Machine learning (ML)-based patch management systems have long been able to prioritize patches based on current threats and business risks. Regular maintenance ensures compliance with PCI DSS, HIPAA and GDPR, while AI automation bridges the gap between detection and response, reducing exposure. Gartner warns that relying on manual processes creates “bottlenecks, delays zero-day response and results in lower-priority patches being applied while actively exploited vulnerabilities remain unaddressed.” Organizations must shift to continuous, automated patching to keep pace with adversaries. Choosing the right patch management solution There are many advantages of integrating gen AI and improving long-standing ML algorithms that are at the core of automated patch management systems. All vendors who compete in the market have roadmaps incorporating these technologies. The GigaOm Radar for Patch Management Solutions Report highlights the technical strengths and weaknesses of top patch management providers. It compares vendors including Atera, Automox, BMC client management patch powered by Ivanti, Canonical, ConnectWise, Flexera, GFI, ITarian, Jamf, Kaseya, ManageEngine, N-able, NinjaOne, SecPod, SysWard, Syxsense and Tanium. The GigaOm Radar plots vendor solutions across a series of concentric rings, with those set closer to the center judged to be of higher overall value. The chart characterizes each vendor on two axes — balancing “maturity” versus “innovation” and feature “play” versus “platform play” — while providing an arrow that projects each solution’s evolution over the coming 12 to 18 months. Gartner advises security teams to “leverage risk-based prioritization and automated workflow tools to reduce time-to-patch,” and every vendor in this market is reflecting that in their roadmaps. A strong patching strategy requires the following: Strategic deployment and automation: Mapping critical assets and reducing manual errors through AI-driven automation. Risk-based prioritization: Focusing on actively exploited threats. Centralized management and continuous monitoring: Consolidating patching efforts and maintaining real-time security visibility. By aligning patching strategies with these principles, organizations can reduce their teams’ workloads and build stronger cyber resilience. Automating patch management: Measuring success in real time All vendors who compete in this market have attained a baseline level of performance and functionality by streamlining patch validation, testing and deployment. By correlating patch data with real-world exploit activity, vendors are reducing customers’ mean time to remediation (MTTR). Measuring success is critical. Gartner recommends tracking the following (at a minimum): Mean-time-to-patch (MTTP): The average time to remediate vulnerabilities. Patch coverage percentage: The proportion of patched assets relative to vulnerable ones. Exploit window reduction: The time from vulnerability disclosure to remediation. Risk reduction impact: The number of actively exploited vulnerabilities patched before incidents occur. Automate patch management — or fall behind Patching isn’t the action item security teams should just get to after other higher-priority tasks are completed. It must be core to keeping a business alive and free of potential threats. Simply put, patching is at the heart of cyber resilience. Yet, too many organizations deprioritize it, leaving known vulnerabilities wide open for attackers increasingly using AI to strike faster than ever. Static CVSS scores have proven they can’t keep up, and fixed cycles have turned into more of a liability than an asset. The message is simple: When it comes to patching, complacency is dangerous — it’s time to make it a priority.

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

Procrastinating about patching has killed more networks and damaged more companies than any zero-day exploit or advanced cyberattack.

Complacency kills — and carries a high price. Down-rev (having old patches in place that are “down revision”) or no patching at all is how ransomware gets installed, data breaches occur and companies are fined for being out of compliance. It isn’t a matter of if a company will be breached but when — particularly if they don’t prioritize patch management.

Why so many security teams procrastinate – and pay a high price

Let’s be honest about how patching is perceived in many security teams and across IT organizations: It’s often delegated to staff members assigned with the department’s most rote, mundane tasks. Why? No one wants to spend their time on something that is often repetitive and at times manually intensive, yet requires complete focus to get done right.

Most security and IT teams tell VentureBeat in confidence that patching is too time-consuming and takes away from more interesting projects. That’s consistent with an Ivanti study that found that the majority (71%) of IT and security professionals think patching is overly complex, cumbersome and time-consuming.

Remote work and decentralized workspaces make patching even more complicated, 57% of security professionals reported. Also consistent with what VentureBeat is hearing from security teams, Ivanti found that 62% of IT and security leaders admit that patch management takes a backseat to other tasks.

The truth is that device inventory and manual approaches to patch management haven’t been keeping up for a while (years). In the meantime, adversaries are busy improving their tradecraft, creating weaponized large language models (LLMs) and attack apps.

Not patching? It’s like taking the lock off your front door

Crime waves are hitting affluent, gated communities as criminals use remote video cameras for 24/7 surveillance. Leaving a home unlocked without a security system is an open invitation for robbers.

Not patching endpoints is the same. And, let’s be honest: Any task that gets deprioritized and pushed down action item lists will most likely never be entirely completed. Adversaries are improving their tradecrafts all the time by studying common vulnerabilities and exposures (CVEs) and finding lists of companies that have those vulnerabilities — making them even more susceptible targets.

Gartner often weighs in on patching in their research and considers it part of their vulnerability management coverage. Their recent study, Top 5 Elements of Effective Vulnerability Management, emphasizes that “many organizations still mismanage patching exceptions, resulting in missing or ineffective mitigations and increased risk.”

Mismanagement starts when teams deprioritize patching and consider manual processes “good enough” to complete increasingly complex, challenging and mundane tasks. This is made worse with siloed teams. Such mismanagement creates exploitable gaps. The old mantra “scan, patch, rescan” isn’t scaling when adversaries are using AI and generative AI attacks to scan for endpoints to target at machine speed.

GigaOm’s Radar for Unified Endpoint Management (UEM) report further highlights how patching remains a significant challenge, with many vendors struggling to provide consistent application, device driver and firmware patching. The report urges organizations to consider how they can improve patch management as part of a broader effort to automate and scale vulnerability management.

Why traditional patch management fails in today’s threat landscape

Patch management in most organizations begins with scheduled monthly cycles that rely on static Common Vulnerability Scoring System (CVSS) severity scores to help prioritize vulnerabilities. Adversaries are moving faster and creating more complex threats than CVSS scores can keep up with.

As Karl Triebes, Ivanti’s CPO, explained: “Relying solely on severity ratings and a fixed monthly cycle exposes organizations to unaccounted risk. These ratings overlook unique business context, security gaps and evolving threats.” In today’s fast-moving environment, static scores cannot capture an organization’s nuanced risk profile.

Gartner’s framework underscores the need for “advanced prioritization techniques and automated workflows that integrate asset criticality and active threat data to direct limited resources toward vulnerabilities that truly matter.” The GigaOm report similarly notes that, while most UEM solutions support OS patching, fewer provide “patching for third-party applications, device drivers and firmware,” leaving gaps that adversaries exploit.

Risk-based and continuous patch management: A smarter approach

Chris Goettl, Ivanti’s VP of product management for endpoint security, explained to VentureBeat: “Risk-based patch prioritization goes beyond CVSS scores by considering active exploitation, threat intelligence and asset criticality.” Taking this more dynamic approach helps organizations anticipate and react to risks in real time, which is far more efficient than using CVSS scores.

Triebes expanded: “Relying solely on severity ratings and a fixed monthly cycle exposes organizations to unaccounted risk. These ratings overlook your unique business context, security gaps and evolving threats.” However, prioritization alone isn’t enough.

Adversaries can quickly weaponize vulnerabilities within hours and have proven that genAI is making them even more efficient than in the past. Ransomware attackers find new ways to weaponize old vulnerabilities. Organizations following monthly or quarterly patching cycles can’t keep up with the pace of new tradecraft.

Machine learning (ML)-based patch management systems have long been able to prioritize patches based on current threats and business risks. Regular maintenance ensures compliance with PCI DSS, HIPAA and GDPR, while AI automation bridges the gap between detection and response, reducing exposure.

Gartner warns that relying on manual processes creates “bottlenecks, delays zero-day response and results in lower-priority patches being applied while actively exploited vulnerabilities remain unaddressed.” Organizations must shift to continuous, automated patching to keep pace with adversaries.

Choosing the right patch management solution

There are many advantages of integrating gen AI and improving long-standing ML algorithms that are at the core of automated patch management systems. All vendors who compete in the market have roadmaps incorporating these technologies.

The GigaOm Radar for Patch Management Solutions Report highlights the technical strengths and weaknesses of top patch management providers. It compares vendors including Atera, Automox, BMC client management patch powered by Ivanti, Canonical, ConnectWise, Flexera, GFI, ITarian, Jamf, Kaseya, ManageEngine, N-able, NinjaOne, SecPod, SysWard, Syxsense and Tanium.

The GigaOm Radar plots vendor solutions across a series of concentric rings, with those set closer to the center judged to be of higher overall value. The chart characterizes each vendor on two axes — balancing “maturity” versus “innovation” and feature “play” versus “platform play” — while providing an arrow that projects each solution’s evolution over the coming 12 to 18 months.

Gartner advises security teams to “leverage risk-based prioritization and automated workflow tools to reduce time-to-patch,” and every vendor in this market is reflecting that in their roadmaps. A strong patching strategy requires the following:

Strategic deployment and automation: Mapping critical assets and reducing manual errors through AI-driven automation.
Risk-based prioritization: Focusing on actively exploited threats.
Centralized management and continuous monitoring: Consolidating patching efforts and maintaining real-time security visibility.

By aligning patching strategies with these principles, organizations can reduce their teams’ workloads and build stronger cyber resilience.

Automating patch management: Measuring success in real time

All vendors who compete in this market have attained a baseline level of performance and functionality by streamlining patch validation, testing and deployment. By correlating patch data with real-world exploit activity, vendors are reducing customers’ mean time to remediation (MTTR).

Measuring success is critical. Gartner recommends tracking the following (at a minimum):

Mean-time-to-patch (MTTP): The average time to remediate vulnerabilities.
Patch coverage percentage: The proportion of patched assets relative to vulnerable ones.
Exploit window reduction: The time from vulnerability disclosure to remediation.
Risk reduction impact: The number of actively exploited vulnerabilities patched before incidents occur.

Automate patch management — or fall behind

Patching isn’t the action item security teams should just get to after other higher-priority tasks are completed. It must be core to keeping a business alive and free of potential threats.

Simply put, patching is at the heart of cyber resilience. Yet, too many organizations deprioritize it, leaving known vulnerabilities wide open for attackers increasingly using AI to strike faster than ever. Static CVSS scores have proven they can’t keep up, and fixed cycles have turned into more of a liability than an asset.

The message is simple: When it comes to patching, complacency is dangerous — it’s time to make it a priority.

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As the demand for data center power surges in Texas, nuclear startup Last Energy has now announced plans to build 30 microreactors in the state’s Haskell County near the Dallas-Fort Worth Metroplex. The reactors will serve a growing customer base of data center operators in the region looking for reliable, carbon-free energy. The plan marks Last Energy’s largest project to date and a significant step in advancing modular nuclear power as a viable solution for high-density computing infrastructure. Meeting the Looming Power Demands of Texas Data Centers Texas is already home to over 340 data centers, with significant expansion underway. Google is increasing its data center footprint in Dallas, while OpenAI’s Stargate has announced plans for a new facility in Abilene, just an hour south of Last Energy’s planned site. The company notes the Dallas-Fort Worth metro area alone is projected to require an additional 43 gigawatts of power in the coming years, far surpassing current grid capacity. To help remediate, Last Energy has secured a 200+ acre site in Haskell County, approximately three and a half hours west of Dallas. The company has also filed for a grid connection with ERCOT, with plans to deliver power via a mix of private wire and grid transmission. Additionally, Last Energy has begun pre-application engagement with the U.S. Nuclear Regulatory Commission (NRC) for an Early Site Permit, a key step in securing regulatory approval. According to Last Energy CEO Bret Kugelmass, the company’s modular approach is designed to bring nuclear energy online faster than traditional projects. “Nuclear power is the most effective way to meet Texas’ growing energy demand, but it needs to be deployed faster and at scale,” Kugelmass said. “Our microreactors are designed to be plug-and-play, enabling data center operators to bypass the constraints of an overloaded grid.” Scaling Nuclear for

Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ; Dallas, TX; Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland. With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116, a bill

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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