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
🧰 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/CCPAoverlays — 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
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Software as a Service (SaaS)
Software accessed as an online service instead of being installed and maintained entirely on the customer’s own computers or servers.
Cybersecurity
The practices and controls used to protect identities, devices, networks, applications, and data from unauthorized access, disruption, or manipulation.
Zero Trust
A security model that does not automatically trust a user or device because of its location. Access is continuously verified and limited to what is necessary.
Identity and Access Management (IAM)
The systems and policies that determine who a user is, what resources they may access, and how that access is authenticated and reviewed.
Multi-Factor Authentication (MFA)
A login control requiring more than one form of verification, such as a password plus an authenticator app, security key, or biometric factor.
API
An application programming interface is a defined way for software systems to exchange data or request functions from one another.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.