Enterprise AI Security Roadmap

The AI Red Teaming roadmap focuses on adversarial testing—prompt hacking, model vulnerabilities, RAG and agent assessment, tools and professional development. This expanded roadmap turns those topics into a complete enterprise AI-security programme covering the full lifecycle: use-case approval, data collection, deployment, monitoring, incident response and retirement.

The central principle is:

Do not secure only the model. Secure the business decision, data, model, retrieval system, agent, tools, application, cloud platform, users and operating process as one connected system.

Executive summary

Operate NIST AI RMF (Govern–Map–Measure–Manage) with ISO/IEC 42001, EU AI Act classification, GDPR/UK GDPR, OWASP Web/LLM/Agentic Top 10 and MITRE ATLAS. Progress through a 48-week path from foundations and governance through data, cloud, model, LLM/RAG, multimodal and agentic controls, explainability, traceability, red teaming and continuous compliance. Use the multi-cloud control matrix and mandatory assurance artefacts as the delivery checklist.

When to use this page

• Use when designing an organisation-wide AI security programme—not only model testing.
• Pair with the full guide: Enterprise AI Security Roadmap.
• Cross-check with AI Red Teaming, DevSecOps, API Security Engineering, Security Engineering and Security & privacy.

What this roadmap covers

Four related architectures:

Architecture	Examples
LLM applications	Chatbots, document assistants, RAG, NL analytics, decision support
Multimodal AI	Text, images, audio, video, documents, screen captures, generated media
Multi-model AI	Routing, cascades, safety classifiers, embeddings, fallbacks, hybrid cloud/on-prem
Agentic / multi-agent	Planning, tools, memory, delegation, workflows, delegated authority

OWASP separates LLM application risks (2025 Top 10) from agentic risks (2026 Top 10: goal hijack, tool misuse, identity abuse, memory poisoning, insecure agent communication, cascading failures).

Nine disciplines of trustworthy AI

Discipline	Core question
AI security	Can the system resist attack, misuse and unauthorised access?
AI safety	Can the system avoid causing unacceptable harm?
Compliance	Which laws, regulations, contracts and standards apply?
Governance	Who makes decisions and who is accountable?
Ethics	Should the system be built and used in this way?
Explainability	Can people understand why an output or decision occurred?
Transparency	Are users told what the system is, what it does and its limitations?
Traceability	Can an outcome be followed through data, model, prompt, retrieval and tools?
Auditability	Can an independent reviewer reconstruct and evaluate what happened?

Accountability connects them: named executive, business, technical, data, model, security and privacy/compliance owners; escalation authority; formal risk acceptance; and a mechanism to stop the system. NIST frames trustworthy AI through validity, safety, security, accountability, explainability, privacy and fairness—balanced by context and risk.

Assurance framework stack

Do not choose one framework and ignore the rest:

1. NIST AI RMF — Govern, Map, Measure, Manage (+ Generative AI Profile)
2. ISO/IEC 42001 — AI Management System (Plan–Do–Check–Act); pair with ISO/IEC 23894 and ISO/IEC 42005
3. EU AI Act — prohibited, high-risk, transparency, GPAI, minimal-risk; maintain a live obligations register
4. GDPR / UK GDPR — lawful basis, minimisation, ADM, DPIA, transfers (ICO accountability guidance)
5. OWASP — Web Top 10 2025, LLM Top 10 2025, Agentic Top 10 2026
6. MITRE ATLAS — threat-informed defence, red-team scenarios, detections

End-to-end control layers

Users → channel security → identity → input/multimodal inspection → PII policy
  → prompt/context isolation → model gateway → RAG/memory → agent/tools/policy
  → output validation → user response or approved action

Across every layer: identity, encryption, policy, logging, tracing, evaluation, lineage, monitoring, incident response, cost controls and audit evidence.

Twelve-stage, 48-week journey

Stage	Weeks	Outcome
1. Foundations	1–4	Understand AI, cybersecurity and secure software
2. Governance and inventory	5–8	Ownership, policy and AI asset visibility
3. Compliance and ethical impact	9–12	Legal, regulatory and societal risk classification
4. Data and privacy security	13–16	Secure training, RAG, prompts, memory and logs
5. Cloud and platform security	17–20	Landing zones, identity and network controls
6. Model lifecycle security	21–24	Models, artefacts, dependencies and suppliers
7. LLM and RAG security	25–28	OWASP LLM Top 10 controls
8. Multimodal and agentic security	29–32	Images, audio, tools, memory and agents
9. Explainability and transparency	33–36	Explanations and user disclosures
10. Traceability and auditability	37–40	Reconstruct every important AI outcome
11. Red teaming and assurance	41–44	Validate controls adversarially
12. Operations and continuous compliance	45–48	Monitor, respond, audit and improve

Stage 1 — Foundations (weeks 1–4)

Study AI fundamentals (transformers, embeddings, RAG, tools, agents, multimodal, routing) and security foundations (CIA, identity, zero trust, threat modelling, cryptography, APIs, supply chain, IR).

Build: small AI app with auth, LLM, vector store, three synthetic docs, a business tool, structured outputs and audit logging.

Exit: explain where data enters, which services and models process it, where it is stored, which identity acts, which controls block unauthorised access and which logs prove what occurred.

Stage 2 — Governance, ownership and inventory (weeks 5–8)

Establish AI steering committee, risk/assurance forum and operations forum. Inventory every use case, model, agent, tool, prompt, RAG index, dataset and endpoint. Publish acceptable-use, prohibited-use, procurement, model-approval, data-use, oversight, security, IR, transparency, evaluation and retirement policies.

Exit: no production AI without business owner, technical owner, risk class, inventory entry, approved model, impact assessment, threat model and deployment decision.

Stage 3 — Compliance, ethics and impact (weeks 9–12)

Build an obligations register (data protection, ADM, AI Act, sector rules, IP, consumer law, accessibility, retention, cross-border, contracts). Run AI impact and ethical assessments; define human-in/on/over/out-of-the-loop oversight.

Exit: impact assessment, DPIA where needed, AI Act classification, ethical assessment, obligations register, oversight design and residual-risk decision.

Stage 4 — Data governance and privacy (weeks 13–16)

Classify prompts, files, embeddings, memory, tool I/O, logs and feedback. Minimise before inference. Build a PII pipeline (scan → classify → detect → mask → policy → process → inspect). Secure RAG (permission-aware retrieval, provenance, deletion propagation) and every memory type (write/read, retention, poisoning detection).

Exit: every data source has owner, classification, purpose, lawful basis, retention, access policy, lineage, deletion procedure, quality assessment and security controls.

Stage 5 — Cloud, identity, network and platform (weeks 17–20)

SSO, MFA, RBAC/ABAC, workload identities, separate agent identities, PAM. Delegated authority for tools (policy → approval → short-lived credential → one action). Private endpoints, segmentation, egress allowlists, encryption, secrets vaults, API authz, rate limits and abuse detection.

Stage 6 — Model lifecycle and supply chain (weeks 21–24)

Assess providers before approval; maintain provenance; protect registries, weights, prompts, MCP servers and CI/CD with signing, SBOM/MBOM and scanning. Treat model upgrades, new tools, prompts, retrieval sources and guardrail changes as material changes requiring regression tests.

Stage 7 — OWASP LLM Top 10 (weeks 25–28)

Implement controls for prompt injection, sensitive disclosure, supply chain, poisoning, improper output handling, excessive agency, system-prompt leakage, vector/embedding weaknesses, misinformation and unbounded consumption. Test guardrail configuration—do not assume it works.

Stage 8 — Multimodal, multi-model and agentic (weeks 29–32)

Inspect images, documents, audio and video for hidden instructions and adversarial content. Harden model routing, policy consistency and security-equivalent fallbacks. Apply OWASP Agentic Top 10 (ASI01–ASI10). High-impact tools require exact action display, authenticated approval, time-limited single-use tokens and immutable audit events.

Stage 9 — Explainability and transparency (weeks 33–36)

Audience-specific explanations (users, operators, engineers, auditors). Prefer citations, evidence, business rules and uncertainty over claiming faithful hidden reasoning. Produce model cards, system cards and AI notices.

Stage 10 — Traceability and auditability (weeks 37–40)

One correlation ID across user, prompt version, model, retrieval, guardrails, tools, approvals and output. Use OpenTelemetry-friendly schemas; minimise sensitive content in logs. Maintain an AI Assurance evidence repository (inventory, assessments, threat models, cards, evaluations, red-team reports, approvals, incidents, retirement).

Stage 11 — Red teaming and assurance (weeks 41–44)

Unit, integration, security, AI red-team and responsible-AI evaluation layers. Map to OWASP, ATLAS, NIST and policy. Gate releases on critical findings, cross-tenant access, high-impact tools, DPIA, cards, evaluations, tracing, kill switch, human approval and residual-risk acceptance.

Stage 12 — Operations and continuous compliance (weeks 45–48)

Monitor operational, quality, security, responsible-AI and compliance signals. Maintain playbooks for leakage, injection, poisoning, unsafe autonomy, supply-chain compromise, bias, deepfakes, cost abuse and audit-log failure. Support disable-agent/tool, revoke credentials, quarantine memory, rollback and forensic preservation.

Multi-cloud control matrix (summary)

Objective	AWS	Azure	Google Cloud
Runtime AI firewall	Bedrock Guardrails	Content Safety / gateway	Model Armor
PII discovery	Macie / custom	Purview / custom	Sensitive Data Protection
Explainability	SageMaker Clarify	Responsible AI dashboard	Vertex Explainable AI
API audit	CloudTrail	Activity / diagnostic logs	Cloud Audit Logs
Security posture	Security Hub	Defender for Cloud	Security Command Center
SIEM	Security Lake / SIEM	Sentinel	Security operations

Define one provider-neutral control catalogue, then map to cloud implementations.

Mandatory assurance artefacts

Medium- or high-risk systems should maintain use-case record, business case, impact and ethical assessments, DPIA, regulatory classification, DFDs, threat model, lineage, model provenance, model/system cards, supplier assessment, security architecture, oversight design, evaluation plan/report, red-team report, residual-risk acceptance, deployment approval, monitoring and IR plans, change log, audit evidence and retirement plan.

Five-level maturity

1. Experimental — ad hoc use, minimal docs
2. Controlled — inventory, approved models, basic guardrails
3. Managed — formal governance, DPIA, CI security, red teaming, cards
4. Measured — continuous evals, automated evidence, quantified residual risk
5. Adaptive — threat-informed controls, real-time policy, automated containment

Capstone

Build a regulated financial-services assistant: multimodal inputs, PII masking, cited RAG, multi-model routing, short-term memory, payment-prep tool with human approval, end-to-end traces, deployable on AWS, Azure or Google Cloud. Test injection (text/PDF/image), cross-customer retrieval, PII leakage, fallback downgrade, tool manipulation, memory poisoning, approval bypass and missing audit events.

Core principle

The final capability is not knowing each framework in isolation. It is taking an ambiguous AI use case and producing a system that is commercially useful, technically reliable, secure by design, privacy-preserving, legally defensible, ethically evaluated, explainable, transparent, traceable, auditable and controllable when something goes wrong.

Key takeaways

• Secure the connected system—decision, data, model, retrieval, agent, tools, app, cloud and process—not the model alone.
• Stack NIST AI RMF, ISO/IEC 42001, EU AI Act, GDPR, OWASP and ATLAS; map once to multi-cloud controls.
• Gate production on owners, assessments, evidence, red-team validation, tracing and a tested kill switch.

What to do next

• Read the full guide: Enterprise AI Security Roadmap.
• Deepen adversarial testing with AI Red Teaming and delivery hardening with DevSecOps.
• Practise related skills: Security Engineering, Responsible AI governance and Security & privacy.