AI Patch Prioritization for Microsoft Zero-Days

Microsoft shipped a relatively small Patch Tuesday in December 2025—57 fixes—and still included the one thing that blows up everyone’s plans: an actively exploited zero-day.

That’s the operational reality most security teams live in now. Even when the patch volume is “light,” the risk isn’t. Attackers don’t care that you’re short-staffed in the last two weeks of December. They care that a single privilege escalation turns an initial foothold into SYSTEM-level control.

This post is part of our AI in Cybersecurity series, and I want to be blunt: patching is no longer just a monthly hygiene task. It’s a detection-and-response problem. The teams that treat it that way—using AI to triage, predict exploitability, and automate safe rollout—move faster than the ones relying on spreadsheets and gut feel.

What happened in Microsoft’s December 2025 Patch Tuesday

Answer first: Microsoft fixed one exploited zero-day and shipped patches for 57 vulnerabilities, while two additional flaws had public proof-of-concept (PoC) exploit code available.

The exploited zero-day is CVE-2025-62221 (CVSS 7.8), affecting the Windows Cloud Files Mini Filter Driver. The key detail isn’t the score—it’s the impact: elevation of privilege to SYSTEM after compromise.

Two other vulnerabilities had PoCs publicly available:

• CVE-2025-54100 (CVSS 7.8) — PowerShell remote code execution (command injection via web content processing)
• CVE-2025-64671 (CVSS 8.4) — RCE in GitHub Copilot for JetBrains code completion tooling

Microsoft assessed those PoC’d issues as lower likelihood of exploitation. I don’t love that framing because it can turn into “we’ll do it next month,” which is exactly how PoC code graduates into incident tickets.

One more number matters for planning: Microsoft issued more than 1,150 patches in 2025. That scale forces a choice—either you automate prioritization, or you accept blind spots.

Why the most dangerous bugs are privilege escalations

Answer first: Privilege escalation vulnerabilities are so damaging because they’re the “step two” that converts a minor breach into full control—fast.

December’s release continued a trend: a heavy concentration of post-compromise elevation of privilege (EoP) issues. This is exactly what you’d expect from modern attack chains:

1. Initial access (phish, stolen token, exposed service, supply chain)
2. Local privilege escalation to admin/SYSTEM
3. Credential dumping, lateral movement, persistence
4. Business impact (ransomware, data theft, sabotage)

EoP bugs are particularly nasty in enterprises because they thrive in the messy middle:

• laptops that miss patches due to travel/offline time n- VDI pools with inconsistent images
• “temporary” local admin exceptions that became permanent
• endpoint controls tuned to avoid breaking productivity apps

CVSS doesn’t capture your real-world blast radius

Answer first: A CVSS 7.8 privilege escalation can be more urgent than a higher-scoring RCE if attackers are already using it—or if your environment makes exploitation easy.

CVSS is useful, but it’s not a queue. A better queue comes from combining:

• Exploit status (active exploitation > PoC > none)
• Exposure (how many assets, how reachable, how privileged)
• Business criticality (domain controllers, CI/CD runners, dev workstations)
• Compensating controls (is LSA protected? is PowerShell constrained? is EDR in block mode?)

This is the exact point where AI earns its keep: it can compute risk faster than humans can argue about it.

AI-driven patch management: what “good” looks like in 2026

Answer first: AI-driven patch prioritization works when it merges vulnerability data, asset context, and live threat signals into a single, explainable patch queue—and then helps execute it safely.

Most organizations already have the raw ingredients:

• vulnerability scans and SBOM-like inventories
• CMDB/asset tags (even if imperfect)
• endpoint telemetry from EDR
• identity logs and privilege assignments
• threat intel feeds (vendor, ISAC, internal)

The failure mode is common: these data sources live in different tools, owned by different teams, and get reconciled manually during “Patch Tuesday week.” AI can reduce that friction by doing three jobs well.

1) Predict exploitability, not just severity

Answer first: The fastest path to fewer incidents is prioritizing the vulnerabilities most likely to be exploited in your environment, not the ones with the scariest descriptions.

A practical AI model (or rules-plus-ML approach) should factor:

• whether exploitation is confirmed in the wild
• whether PoC is public and easy to weaponize
• whether the affected component is commonly present (PowerShell, Office, IDE plugins)
• whether your organization’s telemetry shows precursor behavior (suspicious PowerShell, unusual child processes, token misuse)

For December’s set, a sensible priority order for many enterprises looks like:

1. CVE-2025-62221 (Windows EoP, exploited) — patch ASAP on endpoints and servers where local compromise is plausible
2. CVE-2025-54100 (PowerShell RCE, PoC) — prioritize systems where PowerShell is used heavily or exposed through workflows pulling web content
3. CVE-2025-62554 (Office RCE, critical) — prioritize user endpoints, especially finance and exec populations
4. CVE-2025-64671 (Copilot for JetBrains RCE, PoC) — prioritize developer workstations, CI agents, and any environment where IDEs touch secrets

That ordering isn’t “one size fits all.” The point is: AI can generate an initial ranking in minutes, then humans adjust based on business constraints.

2) Automate patch rollout with guardrails

Answer first: Automation only helps when it’s paired with safe rollout patterns—rings, canaries, rollback, and verification.

The best teams I’ve worked with run patching like a release pipeline:

• Ring 0 (canary): IT + security endpoints, a few representative servers
• Ring 1: a small slice of each business unit
• Ring 2: broad deployment
• Ring 3: exceptions and edge cases

AI adds value by selecting canary populations that are truly representative (hardware models, installed apps, device health), then watching:

• crash rates
• boot failures
• application error spikes
• EDR detections post-patch (sometimes patches change behaviors)

If a rollout creates issues, AI can help identify the common denominator (specific driver version, conflicting agent) faster than a war room can.

3) Verify remediation instead of assuming it

Answer first: A patch isn’t “done” when it’s scheduled—it’s done when you can prove coverage.

Verification should combine:

• OS/build checks
• installed KB or package confirmation
• vulnerability rescan results
• runtime signals (is the vulnerable driver/module still loaded?)

This is especially relevant for privilege escalation bugs like CVE-2025-62221, because partial coverage leaves you with a false sense of security—attackers only need one neglected device with the right permissions path.

Developer tools are now part of the attack surface

Answer first: IDE assistants and AI coding tools expand the blast radius of a compromise because they sit near code, credentials, and deployment pipelines.

The GitHub Copilot for JetBrains flaw (CVE-2025-64671) is a good reminder that “developer experience” tools aren’t harmless. Dev workstations often have:

• access tokens to source control
• cloud credentials cached in CLIs
• secrets in environment variables
• SSH keys
• local copies of proprietary code

Security teams sometimes treat these as second-tier endpoints. That’s a mistake. If you’re building software, your dev fleet is a high-value target set.

Where AI helps defenders specifically in dev environments

Answer first: AI helps by detecting abnormal IDE and CLI behavior, and by stopping risky prompt/tool interactions that expose secrets.

Concrete controls that work:

• anomaly detection for unusual token usage from dev devices
• monitoring for suspicious child process trees spawned by IDEs
• policy that restricts AI tools from reading sensitive directories by default
• secrets scanning that runs locally and in CI

If you’re piloting “agentic” dev assistants, treat them like privileged software: least privilege, audit trails, and rapid patch SLAs.

A December reality check: speed beats perfect planning

Answer first: Patch management success is measured in time-to-risk-reduction, not in how polished the spreadsheet looks.

December is a brutal month for operational security:

• change freezes
• vacation schedules
• end-of-year business deadlines
• reduced on-call coverage

Attackers know this. And an exploited privilege escalation is exactly the kind of vulnerability they’ll lean on because it fits into ransomware and data theft playbooks.

Here’s what works when you need movement within 24–72 hours:

1. Patch the exploited zero-day first (CVE-2025-62221) on the highest-exposure assets
2. Add temporary hardening where patching lags (reduce local admin, tighten credential protections, increase EDR blocking)
3. Prioritize PoC vulnerabilities by role (PowerShell-heavy servers, developer endpoints, Office-heavy user groups)
4. Use AI to track coverage and exceptions so leadership sees reality, not optimism

A practical rule: if exploitation is confirmed, your patch SLA should be measured in days, not weeks.

Next steps: turn Patch Tuesday into an AI-assisted workflow

Security teams don’t need more alerts. They need better decisions, faster—especially when a “light” patch month still includes an exploited zero-day.

If you’re building your 2026 roadmap for AI in cybersecurity, patch prioritization is one of the safest places to start because the ROI is tangible:

• reduced exposure window to zero-day exploitation
• fewer emergency change requests
• clearer reporting to leadership on real risk reduction

If you had to shorten your response time to exploited vulnerabilities by 50% next quarter, what would break first: asset visibility, prioritization, or deployment mechanics? That answer tells you where AI can help fastest.