Rapid7 and OpenAI: Helping Defenders Move at Machine Speed

Laag (27/100): monitoren. Zwaarst wegend: betrouwbaarheid van het signaal en technische ernst.

De priority_score is de Action Urgency Score: een gewogen combinatie van de technische ernst, de exploitatie en de gemeentelijke relevantie.

Wade Woolwine is Senior Director, Product Security at Rapid7. Announcing OpenAI's Trusted Access for Cyber program CIOs and CISOs are telling us the same thing in different ways: Advances in frontier AI are accelerating the threat environment and putting pressure on security operating models built for a different pace. Vulnerabilities can be discovered faster, exploitation windows are shrinking, and attackers are increasingly using automation to move with greater speed and scale. For defenders, this changes the value equation. The premium is no longer only on detecting threats faster after they emerge, but on moving earlier: Reducing exposure, validating risk, strengthening detection, and remediating at scale before attackers can take advantage. This is why Rapid7 is excited to be included in OpenAI’s Trusted Access for Cyber program and their announcement today. OpenAI’s approach recognizes that advanced AI can help verified security teams move faster on legitimate defensive work, from triage and detection to validation, patching, malware analysis, and detection engineering. It also recognizes that some specialized cyber workflows require stronger verification, monitoring, and feedback loops. As Corey Thomas, CEO of Rapid7, shared: “Security leaders are under pressure from every direction: More vulnerabilities, faster exploitation, and increasing business pressure. Through OpenAI’s Trusted Access for Cyber program, Rapid7 is exploring more ways to accelerate the shift from reactive to preemptive security. To stay ahead of attackers, defenders must proactively reduce exploitability and detect with machine-scale speed and precision. We’re working with OpenAI to equip security teams with advanced capabilities that will meaningfully improve their cyber resilience.” AI in security: Not just faster discovery For Rapid7, this moment is about more than faster vulnerability discovery. AI is creating new pressure across the entire security lifecycle, from vulnerability validation, prioritization, disclosure, and remediation to threat and exploitation detection. Security infrastructure built for human-speed discovery now needs to operate in a machine-speed world, with enough context, governance, and accountability to help defenders act with confidence. Finding risk is only the beginning. Security teams need to understand which vulnerabilities and misconfigurations are truly exploitable, which systems and business services are affected, what compensating controls are in place, how remediation should be prioritized, and where detection coverage is needed. CISOs also need confidence that advanced AI is being applied responsibly, with clear guardrails, measurable outcomes, and accountability. Our work with OpenAI will help us explore how frontier AI can strengthen three critical areas. First, it can support the identification of vulnerabilities in our own products and code earlier in the development lifecycle. By accelerating secure code review, surfacing risky patterns, supporting root cause analysis, reviewing patches, and giving engineering teams faster feedback, AI can help reduce risk before issues reach production. Second, it can advance vulnerability research and exploitation analysis. Rapid7 has long-standing expertise in vulnerability intelligence, exploitability research, and offensive security with Rapid7 Labs . Frontier AI can help researchers reason across unfamiliar code, map affected surfaces, build safe reproduction harnesses, validate severity, and turn findings into practical remediation guidance. Third, it can expand AI-driven red-teaming. As AI becomes more embedded in enterprise systems and security operations, it must also be tested adversarially. We see an opportunity to use AI to strengthen red-team workflows, explore attack paths, validate controls, and help defenders understand where exposure could become real-world risk. Artificial intelligence in use at Rapid7 We are already seeing this potential inside our own security operations work. In support of our Agentic SOC initiatives, Rapid7 has designed and implemented a system that uses machine learning to surface threat- and risk-relevant events from raw log and telemetry data. By using frontier AI models, including OpenAI’s GPT-5.5, to support initial triage and escalate only relevant events to SOC analysts, we have seen a 25% reduction in time spent chasing false-positive events in the queue. This is not about replacing human expertise. It is about giving defenders better leverage in a world where attackers, businesses, and technology are all moving faster. The shift from reactive to preemptive security, and from human-scale processes to machine-scale defense, is not a marketing reframe. It is becoming the only viable path for teams that need to anticipate where attackers will move next, prioritize the exposures that actually matter, and respond at the speed of modern attacks. AI may accelerate discovery, but cyber resilience depends on what happens after discovery. Customers need to unify their data, apply AI with the right context, drive remediation at scale, and translate security activity into measurable outcomes. That is where Rapid7 is focused. Across the Command Platform , Rapid7’s AI capabilities are built to help security teams detect threats and anomalies at scale, reduce noise, optimize SOC workflows, and make faster, more confident decisions. By unifying Exposure Management and Detection and Response on the Command Platform, and combining AI-driven operations with the depth of expertise we have built over 25 years, Rapid7 is giving customers a more coherent way to reduce risk, disrupt attackers, and build durable cyber resilience. Learn more about Rapid7’s AI capabilities.