Secure the Org with AI — and Secure AI Itself (Guardrails, Ops, Evidence)
AI Cybersecurity has two missions:
1) AI for Security — use ML/LLMs to detect, explain, and remediate threats faster.
2) Security for AI — harden data, models, prompts, tools, and pipelines so AI can be trusted.
SolveForce builds both sides as a system: governed data → ML/LLM services (detections, copilots) → SOAR automation → guarded RAG with cite-or-refuse → secure MLOps pipelines → runtime guardrails—wired to SIEM so you can prove safety and efficacy.
Related pages:
📊 Evidence/Automation → /siem-soar • 🚨 IR → /incident-response • 🧪 Exercises → /tabletop
🔐 Identity/Access → /iam • /pam • /ztna • /nac
🔑 Custody → /key-management • /secrets-management • /encryption
📚 Governance → /data-governance • 🔏 Privacy → /dlp
☁️ Infra → /cloud • 🧱 Delivery → /infrastructure-as-code • 🧠 Retrieval → /vector-databases
🎯 Outcomes (Why SolveForce AI Cybersecurity)
- MTTD/MTTR down — AI triages, explains, and launches playbooks; analysts focus on high-value work.
- Precision up, noise down — ML detections tuned with labeled corpora and feedback loops.
- Secure AI stack — models, prompts, tools, and data are hardened against leakage and abuse.
- Provable controls — model cards, lineage, approval trails, and citations export to SIEM for audits.
- Faster response — safe automation (with guardrails) closes tickets and rotates keys at machine speed.
🧭 Scope (What We Build & Operate)
AI for Security
- Detections — anomaly, UEBA, phishing/fraud, malware triage, alert de-dup, risk scoring.
- SOC Copilot — guarded RAG over runbooks, tickets, threat intel; proposes actions with citations; refuses when evidence is insufficient. → /vector-databases
- Forensics assist — summarization and pivot suggestions across logs/PCAPs/edr telemetry.
- SOAR integration — isolate/revoke/rekey/patch/tune WAF rules with approvals. → /siem-soar
Security of AI
- MLOps security — dataset governance, PII minimization, data contracts, lineage, DQ tests; signed artifacts/SBOM; secretless CI/CD. → /data-governance • /infrastructure-as-code
- Model hardening — prompt-injection defenses, tool-use scopes, output filters, jailbreak & exfil protection, model sandboxing.
- Runtime guardrails — allow-listed tools, policy checks, DLP redaction, cite-or-refuse policy, hallucination tests. → /dlp
- Key custody & secrets — KMS/HSM keys, envelope encryption, vault-issued tokens, short-lived credentials. → /key-management • /secrets-management
🧱 Building Blocks (Spelled Out)
1) Data & Feature Governance
- Contracts, labels (PII/PHI/PAN/CUI), lineage, quality gates; feature store with provenance and retention.
- Regional perimeters; Private Endpoints only for training/serving stores.
2) Detection Models
- Hybrid detectors (rules + ML):
- UEBA (identity/device anomalies), lateral movement signals, phishing/fraud, DNS/HTTP/SaaS exfil patterns, cloud IAM misuse.
- Feedback loops from analysts; thresholding per tenant/domain.
3) Guarded RAG (Security Copilot)
- Pre-filters (labels/ACL/region) before ANN search; ontology for acronyms/IoCs; answers must cite sources or refuse.
- Red team prompts and store a refusal ledger for safety audits.
4) LLM App Hardening (OWASP LLM Top 10)
- Prompt isolation, tool scopes, output controls, content safety checks, rate limits/quotas, audit trails.
- Token-level or semantic DLP for responses; allow-listed URLs/APIs only.
5) MLOps Supply Chain
- Model registry with signatures, SBOM/attestations; dataset versioning; reproducible training; policy gates in CI.
- Canary & shadow deployments; rollout rings with auto-rollback on SLO breach.
6) Zero-Trust Everywhere
- SSO/MFA + device posture; ZTNA per app/session; PAM JIT admin with recording; NAC at ports/Wi-Fi. → /ztna • /pam • /nac
🧰 Reference Architectures (Choose Your Fit)
A) SOC Copilot + SOAR
Guarded RAG over runbooks/tickets; inline triage of alerts; one-click approved actions (isolate host, rotate keys, block IP/domain); auto-drafts IR notes with citations.
B) Cloud Threat Brain
Detectors for IAM drift, public exposure, key leakage; graph of resources/roles; auto-open POA&M and PRs to fix drift.
C) Email/Phishing + Fraud Defense
LLM classifiers + rules; brand and DMARC/ARC checks; link sandbox; orchestration to auto-quarantine and open cases.
D) AI App Security Gateway
Prompt firewalls, tool whitelists, DLP redaction, output filters, audit trails; cite-or-refuse enforcement; safety scorecards.
E) Malware & Triage Assist
Embedding search over known samples + file behavior; LLM for readable summaries; safe “what next” playbooks with approvals.
📐 SLO Guardrails (Measure What Matters)
| Domain | KPI / SLO | Target (Recommended) |
|---|---|---|
| Detection | MTTD (Sev-1 via SIEM correlation) | ≤ 5–10 min |
| Precision / Recall (gold set) | ≥ 92–95% / ≥ 85–95% | |
| Response | MTTC (containment start) | ≤ 15–30 min |
| Copilot | Citation coverage | = 100% |
| Refusal correctness | ≥ 98% | |
| Model | Drift detection to ticket | ≤ 30–60 min |
| P95 latency (RAG answer) | ≤ 2–6 s | |
| Safety | Prompt-injection escape rate | ≤ 0.5–1.0% (red-team set) |
| Evidence | Completeness (changes/incidents) | = 100% |
SLO breaches auto-open tickets and trigger SOAR fallbacks (disable auto-action, human-in-the-loop, roll back model/prompt). → /siem-soar
🔒 Compliance & Standards
- NIST AI RMF, ISO/IEC 42001 (AI management), OWASP Top 10 for LLM Apps.
- SOC 2 / ISO 27001 — access/change/logging evidence.
- HIPAA / PCI / GDPR/CCPA overlays — PII/PHI minimization, DLP/tokenization, lawful processing & residency.
📊 Observability & Evidence
- Model cards (purpose, data, metrics, limits), experiment lineage, approvals.
- Prompt & tool logs, citations, refusal ledger; safety events (injection detected, jailbreak blocked).
- SOAR actions: proposed → approved → executed → rollback trail; who/what/when/why.
- Cost: $/inference, GPU hours, data scan $/GB; FinOps dashboards. → /finops
All streams feed SIEM; exports generate auditor packs on demand. → /siem-soar
🛠️ Implementation Blueprint (No-Surprise Rollout)
1) Use-cases & SLOs — pick detections/copilots; define success.
2) Data & governance — contracts, labels, lineage, DQ tests; feature store. → /data-governance
3) Platform — vector DB, model registry, prompt store, safety gateway; GPU/edge footprint.
4) Guardrails — cite-or-refuse, pre-filters, tool scopes, allow-listed actions, human-in-the-loop.
5) Integrations — SIEM/SOAR, EDR/NDR, cloud APIs, ticketing, vault/KMS.
6) Pilot & rings — shadow → advisory → supervised automation → partial auto → full auto; rollback criteria.
7) Operate — SLO dashboa