Treat models like software-and-data supply chains, not magic boxes—and wire security into every hop.

Start with provenance. Track where training and fine-tuning data comes from, record licenses, and maintain bill-of-materials for models (MBOM) and prompts. Align your program to an auditable framework—ISO/IEC 42001 gives you a certifiable AI management system to anchor governance, while NIST’s Generative AI Profile maps practical risk controls across the lifecycle. Build intake gates for third-party models, embeddings, and plug-ins, and refuse components without signatures or documentation. iso.org+1

Engineer Guardrails Where AI “Acts”
As agents gain “computer use” powers, least-privilege becomes non-negotiable. Fence tool access with allowlists, API tokens scoped to single tasks, egress filtering, and time-boxed credentials. Require sandboxed browsers/VMs for any screen-automation. Follow joint NCSC-UK/CISA guidance: design for secure defaults, protect model secrets, and log every tool invocation for audit and rollback. NCSC

Defend the Data Plane (and Your RAG)
Most leaks happen through context, not weights. Apply content classification before retrieval, mask or tokenize sensitive fields, and adopt “grounding policies” that forbid retrieval from red-tagged sources. Validate documents on ingestion to blunt prompt-injection and data-poisoning—CISA’s “Deploying AI Systems Securely” spells out operational mitigations for externally sourced components. For public-facing apps, rate-limit, cache, and monitor unusual retrieval patterns. CISA

Red-Team the Model, Then Monitor Like AppSec
Treat LLM apps as a new web tier. Test for the OWASP Top 10 for LLMs—prompt injection, sensitive info disclosure, overbroad tool calls, and unbounded consumption—before go-live, then continuously once usage shifts. Map adversarial techniques with MITRE ATLAS to exercise jailbreaks, data exfil paths, and model-stealing attempts; add detectors and response playbooks when findings recur. owasp.org+1

Secure the Runtime and Keys
Separate inference from secrets: keep API keys, signing keys, and vector-store credentials in a KMS/HSM; rotate aggressively. Enforce mTLS between the app, orchestrator, retriever, and model endpoints; pin certificates for internal hops. Isolate tenants at the vector index level. Require human approval (or policy engines) for high-risk actions, and maintain kill-switches that can cut tool access within seconds. Align secure development and release to NIST’s SSDF profile for GenAI (SP 800-218A). NIST Computer Security Resource Center

Roadmap Signals for 2026

• ISO/IEC 42001 certification becomes a buyer checklist item for larger enterprises. iso.org

• Organizations standardize LLM AppSec testing using OWASP 2025 controls and ATLAS-based playbooks. owasp.org+1

• NIST’s Generative AI Profile and SSDF-GenAI profile show up in RFPs and audit plans. NIST+1

4. Closing Thoughts
   Securing internal AI is less about one perfect model and more about disciplined plumbing: provenance in, least-privilege out, and eyes on the wire. Build like it’s a new application tier—because it is.

References

• NIST — “Artificial Intelligence Risk Management Framework: Generative AI Profile” — https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence

• ISO — “ISO/IEC 42001: AI Management Systems” — https://www.iso.org/standard/42001

• UK NCSC & CISA — “Guidelines for Secure AI System Development” — https://www.ncsc.gov.uk/files/Guidelines-for-secure-AI-system-development.pdf

• CISA/NSA (Joint) — “Deploying AI Systems Securely (CSI)” — https://media.defense.gov/2024/Apr/15/2003439257/-1/-1/0/CSI-DEPLOYING-AI-SYSTEMS-SECURELY.PDF

• OWASP — “Top 10 for LLM Applications (2025)” — https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-v2025.pdf

Authors & Co-Editors
Serge Boudreaux — AI Hardware Technologies
Montreal, Quebec
Peter Jonathan Wilcheck — Co-Editor
Miami, Florida