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Executive Summary
AI accelerates attacks across reconnaissance, intrusion, and exploitation. This playbook outlines defensive measures you can implement and evaluate within 90 days.
Introduction
Artificial intelligence has fundamentally changed the tempo of cybersecurity. Attackers are no longer limited by human speed — they now use AI to automate phishing, reconnaissance, vulnerability scanning, and social engineering at scale. What once took days can now happen in minutes.
At the same time, organizations are integrating AI into operations, often without full visibility or governance. Third-party large language models (LLMs), AI assistants, and “shadow AI” tools expand the attack surface and introduce new vectors for data leakage and model manipulation.
This playbook provides a practical framework for defending at machine speed. It translates current AI-driven threat trends into concrete security actions you can implement within 90 days — enabling you to protect systems, assist analysts, and secure the AI itself.
Defending at Machine Speed
AI accelerates cyberattacks. Automated phishing, reconnaissance, vulnerability scanning, and deepfake social engineering have made large-scale attacks faster and cheaper to run. Many organizations have reported direct exposure to AI-enabled threats over the past year.
Expanding Attack Surface
Third-party LLMs and unvetted “shadow AI” tools increase supply-chain and data-handling risk. Every AI system — internal or external — should be treated as part of the attack surface.
A Three-Pillar Defense Model
1. Protect — Detect and Prevent in Real Time
Integrate AI into threat intelligence, detection, and enforcement. Automate prevention across endpoints, networks, and cloud to improve visibility, reaction time, and accuracy.
2. Assist — Augment Operations
Use AI to filter and enrich alerts, summarize investigations, and automate routine playbooks under analyst supervision. The goal: fewer tickets, faster resolution, and higher analyst throughput.
3. Secure-AI — Protect Models and Data
Apply the same rigor to models and AI-enabled systems as production workloads. Add controls for:
- Prompt injection and data leakage
- Model poisoning and unauthorized output
- Restricted agent permissions and data boundaries
First 90 Days
Days 0–30: Map and Govern
- Inventory all AI tools and dependencies.
- Publish an Acceptable Use Policy for AI.
- Establish an AI Risk Review Board.
- Deploy egress monitoring for LLM traffic.
Days 31–60: Pilot and Harden
- Pilot AI-assisted alert triage; track MTTR improvement.
- Add behavior-based phishing detection.
- Build an AI app registry with risk ratings.
- Red-team LLM prompts and data egress channels.
Days 61–90: Automate and Scale
- Automate two high-volume playbooks with human approval checkpoints.
- Enforce guardrails: PII masking, least-privilege agent access, and approval workflows.
- Integrate AI checks into SDLC stages (threat modeling, deployment, monitoring).
- Report business-aligned outcomes (risk reduction, resilience, MTTR).
Procurement and Policy Checklist
Data Handling
- Retention, finetuning, deletion, and residency rules.
Security Controls
- SSO/MFA, RBAC/ABAC, audit logging, IP allow-lists, rate limits.
Model Risk
- Red-team reports, jailbreak resilience, input/output filters, bias and variance evaluation metrics.
Operational Readiness
- SLAs, incident-response commitments, SIEM log export capability.
Exit Strategy
- Data/model portability and clear decommissioning steps.
Metrics That Matter
- MTTD/MTTR for AI-detected incidents
- Alert compression ratio and cases per analyst shift
- Detection precision and false-positive rate
- Rate of blocked sensitive-data egress attempts to LLM endpoints
- Model integrity metrics (drift, anomalies, red-team issues closed)
Takeaway
Integrate AI into core controls across protection, operations, and model security. Validate each step with measurable outcomes before scaling.
