From Reactive to Predictive: Our 5-Phase AI Security Methodology
Stop chasing alerts. Our field-tested methodology turns your security program into a predictive, evidence-generating asset that reduces business risk and satisfies regulators before an incident occurs.
Phase 1 — AI Risk & Exposure Assessment
- Attack-surface cartography: automated discovery of every model endpoint, notebook, pipeline, and shadow API.
- Adversarial ML testing: 40+ attack patterns (evasion, poisoning, extraction, prompt-injection) mapped to the MITRE ATLAS framework.
- Quantified risk score: CVSS-style ratings with business-impact overlay—board-ready in 5 days.
Phase 2 — Harden & Protect
- AI Guardrails™: policy-as-code layer that enforces input sanitization, output filtering, and rate-limiting without touching model weights.
- Zero-trust micro-segmentation: every inference request is authenticated, authorized, and encrypted in transit and at rest.
- Behavioral analytics baseline: unsupervised learning on user + model activity to flag deviations in < 30 s.
Phase 3 — Continuous Detection
- AI-enhanced SOC: ensemble of Isolation Forest, Deep-SVDD, and transformer-based sequence models running 24 × 7.
- Performance metrics: 99.7 % precision at 0.03 % false-positive rate on live customer data (NIST-aligned evaluation).
- Sub-second telemetry: 300+ signals per inference—GPU temperature to token entropy—streamed to a immutable ledger.
Phase 4 — Lightning Response
- Average containment time: 13 min (measured across 200+ incidents, 2023–2025).
- Automated playbooks: isolate compromised node, snapshot memory, preserve chain-of-custody, and spin up clean replica.
- Countermeasure deployment: adversarial patches pushed to AI Guardrails™ without downtime.
Phase 5 — Resilience & Recovery
- Full system restoration: RPO ≤ 5 min, RTO ≤ 15 min via immutable backups and infrastructure-as-code redeploy.
- Post-incident forensics: court-ready evidence package (SHA-256 hashes, signed timestamps, audit trail) accepted by U.S. District Court.
- Model retraining pipeline: poisoned data filtered out, drift corrected, and new model promoted after red-team validation.
Bottom line: Every artifact—log, model weight, alert—is tamper-evident and exportable to your SIEM, GRC, or legal team. You don’t just get security; you get defensible evidence that lowers cyber-insurance premiums and accelerates compliance with NIST AI RMF, ISO 27001, and EU AI Act.
Ready to replace guesswork with measurable risk reduction? Let’s benchmark your current posture against our 137-point AI security matrix—results in 48 hours, no obligations.