Most of what has been written about AI and vulnerability management focuses on speed: how much faster frontier AI models can scan code, find weaknesses, design patches, and build exploits than any human team. That part is true, and it matters to how we remediate vulnerabilities.
The more challenging question is what happens after the scan? Frontier AI models are about to hand every security team a far larger set of findings than they have ever had to work through. The real test for chief information security officers (CISOs) and IT security leaders is not how fast they can patch. It is whether they can keep the balance between patching quickly and patching correctly, at a scale no human review process was built for.
This shift creates both new challenges and new opportunities. The same AI capabilities accelerating cyberattackers are also enabling defenders to identify exposures earlier, automate remediation, and build more resilient security programs.
Not every vulnerability will be patched in time, so the controls that limit what an attacker can do by defense in depth matter more than ever. CISOs can dramatically improve the security posture of their Microsoft infrastructure by deploying Microsoft Baseline Security Mode (BSM), building on how Microsoft protects Microsoft.
How do frontier AI models change vulnerability management for CISOs?
Microsoft uses frontier AI models to find vulnerabilities in its code base and mitigate them at controlled pace. Potential vulnerabilities are reviewed on validity, severity, and potential impact. As we have explained in previous blogs, many steps in our vulnerability handling and disclosure processes now are AI-powered, allowing them to scale.
Most vulnerabilities in cloud software are being mitigated by Microsoft without customer intervention. For on-premises Microsoft software, customers should continue to expect a substantial increase in the number of vulnerabilities released on Patch Tuesdays relative to historic volumes before the advent of frontier AI models earlier this year—September 2026 saw a record number, close to 1,000.1
Due to the non-deterministic nature of AI models, different runs by the same model or with different models may yield different results. With better models becoming available over time, AI-powered vulnerability scanning of our existing code base and new code should become part of our standard security assurance.
We use a ‘harness’ layer around AI models in vulnerability scanning for better results. This layer controls how models access code, validate outputs, and integrate findings into triage and remediation workflows. Microsoft has expanded the use of harnesses in scanning its code bases across all the engineering groups. One of these harnesses, codename MDASH, has now also been made available to customers. In addition to AI-powered vulnerability scanning, our internal Red Teaming engagements now leverage AI, enhancing the team’s capacity to find weaknesses in controls and boosting the efficiency and speed of the operations.