90-Day Implementation Plan

Days 1-30: Audit and Baseline

Week 1: Inventory all enterprise AI platform access. Document who has ChatGPT Enterprise, Copilot, Claude, or similar tools. Identify which users can export data and what data sources they can access.

Week 2: Establish usage baselines. Analyze 30 days of activity logs to understand normal patterns by department and role. Flag any existing anomalies for investigation before you implement automated alerting.

Week 3: Update phishing simulations. Replace outdated templates with AI-quality content that mirrors WormGPT and FraudGPT capabilities. Include business email compromise scenarios using proper organizational terminology and plausible urgency.

Week 4: Run baseline simulations across all user segments. Track performance by department. Identify which teams face highest risk and need targeted training.

Days 31-60: Detection and Controls

Week 5: Deploy behavioral monitoring for enterprise AI platforms. Configure alerts for sudden usage spikes, unusual export activity, or repeated content policy violations. Set thresholds based on Week 2 baselines.

Week 6: Audit email security stack. Measure what percentage of inspection depends on signature matching versus behavioral analysis. Prioritize vendors that offer AI-aware detection capabilities.

Week 7: Implement multi-modal verification for high-risk transactions. Wire transfers, vendor payment changes, and privilege escalation requests should require out-of-band confirmation regardless of how legitimate the request appears.

Week 8: Update incident response playbooks. Add specific procedures for suspected AI-generated attacks. Include questions analysts should ask: Does this match known templates? Does the delivery pattern deviate from normal sender behavior? Are there infrastructure anomalies like self-addressed emails or suspicious file types?

Days 61-90: Validation and Intelligence

Week 9: Run AI-specific red team exercises. Simulate WormGPT and FraudGPT capabilities without reproducing criminal tools. Test whether phishing filters catch AI-quality lures, whether finance teams verify urgent requests, and whether analysts recognize AI-generated social engineering.

Week 10: Document gaps identified in red team exercises. Prioritize remediation based on business impact. Financial transaction controls may matter more than general phishing detection depending on your risk profile.

Week 11: Establish threat intelligence feedback loops. Connect dark web monitoring services to security operations. Create templates that translate technical indicators into business language for leadership reporting.

Week 12: Review metrics from Days 1-90. Compare phishing simulation performance, detection coverage percentages, and mean time to incorporate new intelligence. Report findings to leadership with specific budget requests for remaining gaps.

90-Day AI Threat Readiness Checklist

Inventory all enterprise AI platform access and export capabilities
Establish behavioral baselines for AI usage by department and role
Update phishing simulations with AI-quality content examples
Deploy automated monitoring for AI platform usage anomalies
Audit email security stack for behavioral detection coverage (target: 90%+)
Implement multi-modal verification for high-risk transactions
Update incident response playbooks with behavioral anomaly procedures
Run AI-specific red team exercises to test detection capabilities
Establish threat intelligence feedback loops for dark web monitoring
Measure and report phishing resilience rate (target: below 5% for high-risk groups)

Your organization doesn't control whether WormGPT, FraudGPT, or their successors exist. You control whether they succeed when deployed against your environment. That requires treating enterprise AI as infrastructure requiring governance, not just productivity tools requiring procurement.

Microsoft blocked the September 2024 AI-obfuscated campaign (16 months prior) because their detection systems focused on behaviors, not signatures. The campaign used novel code generation techniques. The obfuscation patterns were sophisticated. But the delivery anomalies, infrastructure characteristics, and file handling violations remained consistent with known phishing operations. Behavioral detection worked. Signature matching would have failed.

Your security team needs the same shift. Detection frameworks that identify what attacks do matter more than recognizing which AI tool generated them. The 90-day plan provides the infrastructure. Execution determines whether your environment treats AI-generated threats as undetectable or simply different.

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