Q: What are the main steps in the 90-day playbook for how banks remediate AI-discovered vulnerabilities? A: The 90-day playbook explains how banks remediate AI-discovered vulnerabilities by stabilizing and triaging findings, building an automated “fix factory,” and then hardening and sustaining controls. Days 0–30 centralize and score findings, deduplicate, set SLOs and form fix squads; days 31–60 automate PRs, adopt golden baselines and batch rollouts; days 61–90 shift left, auto-verify fixes and contain blast radius. Q: How do banks triage AI-generated vulnerability findings quickly? A: Banks pull all scanner findings into a single backlog tied to asset IDs and owners, score by exploit signals, internet exposure, data sensitivity and lateral-movement potential, and deduplicate by root cause. They set clear SLOs (for example critical internet-facing: 7 days), stand up fix squads pairing app engineers with security, and pause low-risk churn while they burn down criticals. Q: Which automation tools and practices do banks use to fix routine AI-detected issues? A: Banks use bots to open automated pull requests for known vulnerabilities, run dependency tools like Renovate or Dependabot, and enforce policy-as-code checks with CI gating for criticals only. They also auto-verify fixes by rescanning and rely on automation for the first 80% of routine upgrades so engineers can focus on root-cause removal. Q: Why do banks use batch rollouts and canary releases when deploying patches identified by AI scanners? A: Batch rollouts patch once per service family and then promote changes across clusters to reduce repeated effort and propagate fixes at scale. Canary releases send updates to a small percentage of traffic while teams monitor error and latency budgets, limiting blast radius before full deployment. Q: What metrics should banks track to measure remediation progress? A: Key metrics include mean time to remediate (MTTR) by severity and business unit, backlog burn rate versus new inflow, percent of auto-fixed issues and time saved per fix type, and exposure time for internet-facing criticals. Monitoring duplicate and false-positive rates after tuning shows whether triage and automation are effectively reducing noise. Q: How can banks control costs while responding to a flood of AI-found vulnerabilities? A: Banks throttle scan frequency, consolidate tools to reduce duplicates and license costs, and prioritize automation to handle routine fixes first. They standardize base images and shared libraries to “fix once, propagate many,” use time-bound exception policies for hard vendor issues, and bring in contractors mainly for rote tasks while funding automation capacity. Q: How should banks secure third-party and legacy systems flagged by AI tools? A: Banks demand SBOMs and patch SLAs from vendors and may tie payment milestones to fix delivery, while isolating legacy applications behind strong gateways and monitoring until upgrades are possible. They also wrap systems with compensating controls such as WAF rules, EDR hardening, and strict firewall policies while permanent patches land. Q: What governance and communication practices speed up remediation after AI discovers vulnerabilities? A: Clear, plain-language policies that state what to fix, by when, and who decides exceptions, combined with mapping every finding to a named owner within minutes, support faster fixes. Weekly executive reporting, tabletop drills, and assigning a security champion per team keep accountability and ensure resources are aligned to reduce risk.