Summary / Need
To strengthen focus and detection/response against AI-enabled threats and to implement monitored guardrails for enterprise generative AI usage
Expected Outcomes
Accelerate detection and triage: Implement AI-assisted alert enrichment (context correlation, reputation checks, summarization) and tune detections to reduce noise and improve prioritization
Expand AI threat coverage: Build and maintain detections, correlations, and playbooks for AI-enabled threats (deepfakes, synthetic phishing/impersonation, prompt injection, risky plugins/connectors, and anomalous AI tool usage), with routine testing and tuning.
Operationalize AI monitoring and response: Establish monitoring for AI tools (identity, device, data, network, audit/DLP signals) and publish AI incident response runbooks with escalation criteria, evidence standards, and tabletop validation.
Measures of Success (First 6–12 Months)
Detection catalog in production: Publish an AI threat detection catalog mapped to telemetry sources and deploy an initial prioritized detection set with a monthly tuning cadence.
Faster, cleaner triage: Reduce repeat false positives and improve time-to-triage/time-to-escalation for AI-related alerts through enrichment and tuning.
Monitored guardrails: Stand up baseline monitoring and anomaly thresholds for approved AI tools and deliver recurring executive-ready reporting on risky usage patterns and remediation.
Validated response capability: Publish AI-focused IR runbooks and validate via tabletop exercises; feed lessons learned into playbooks and detection tuning.