AI Security
Security architecture for AI: prompt injection, training data protection, model exfiltration, and guardrails.
On This Page
| 1 | Overview | 2 | Core Principles |
| 3 | Implementation Guide | 4 | Governance Checkpoints |
| 5 | Recommended Patterns | 6 | Anti-Patterns to Avoid |
| 7 | AI Augmentation Extensions | 8 | Related Sections |
| 9 | References |
Overview
Security architecture for AI: prompt injection, training data protection, model exfiltration, and guardrails.
This document is part of the AI-Native Architecture body of knowledge within the Ascendion Architecture Best-Practice Library. It provides comprehensive, practitioner-grade guidance aligned to industry standards and extended for AI-augmented, agentic, and LLM-driven design contexts.
Core Principles
1. Intentional Design for AI Security
Every aspect of ai security must be deliberately designed, not discovered after deployment. Document design decisions as ADRs with explicit rationale.
2. Consistency Across the Portfolio
Apply ai security practices consistently across all systems. Inconsistent application creates governance blind spots and makes incident investigation unpredictable.
3. Alignment to Business Outcomes
AI Security practices must demonstrably contribute to business outcomes: reduced downtime, faster delivery, lower operational cost, or improved compliance posture.
4. Evidence-Based Quality Assessment
Quality of ai security implementation must be measurable. Define specific metrics and collect evidence continuously — not only at audit or review time.
5. Continuous Evolution
Standards for ai security evolve as technology and threat landscapes change. Schedule quarterly reviews of applicable standards and update practices accordingly.
Implementation Guide
Step 1: Current State Assessment
Document the current state of ai security practice: what is implemented, what is missing, what is inconsistent across teams. Use the governance/scorecards section for a structured assessment framework.
Step 2: Gap Analysis Against Standards
Compare current state against the standards in this section and applicable frameworks (TOGAF 9.2 Architecture Governance Framework, COBIT 2019). Prioritize gaps by business impact and remediation effort.
Step 3: Design the Target State
Define the target ai security state: which patterns will be adopted, which anti-patterns eliminated, which governance mechanisms introduced. Express as a time-bound roadmap.
Step 4: Incremental Implementation
Implement ai security improvements incrementally: pilot with one team or system, measure outcomes, refine the approach, then expand. Avoid big-bang transformations.
Step 5: Validate and Iterate
Measure the impact of implemented changes against defined success criteria. Incorporate lessons learned into the practice standards. Contribute improvements back to this library.
Governance Checkpoints
| Checkpoint | Owner | Gate Criteria | Status |
|---|---|---|---|
| Current State Documented | Solution Architect | AI Security current state assessment completed and reviewed | Required |
| Gap Analysis Reviewed | Architecture Review Board | Gap analysis reviewed and prioritization approved | Required |
| Implementation Plan Approved | Enterprise Architect | Target state and roadmap approved by ARB | Required |
| Quality Metrics Defined | Solution Architect | Measurable success criteria defined for ai security improvements | Required |
Recommended Patterns
Reference Architecture Adoption
Start from an established reference architecture for ai security rather than designing from scratch. Adapt to organizational context rather than rebuilding proven foundations.
Pattern Library Contribution
When your team solves a recurring ai security problem with a novel approach, document it as a pattern for the library. This compounds organizational knowledge over time.
Fitness Function Testing
Encode ai security standards as automated architectural fitness functions — tests that run in CI/CD and fail builds when standards are violated. This makes governance continuous rather than periodic.
Anti-Patterns to Avoid
⚠️ Standards Theater
Documenting ai security standards in architecture policies that no one reads and no one enforces. Standards without automated validation or governance gates are not operational standards.
⚠️ Copy-Paste Architecture
Adopting another organization's ai security patterns wholesale without adapting to organizational context, team capability, or regulatory environment. Always adapt; never just copy.
AI Augmentation Extensions
AI-Assisted Standards Review
LLM agents analyze design documents against ai security standards, generating structured gap reports with cited evidence and suggested remediation approaches.
Note: AI review accelerates governance but does not replace expert architectural judgment. Use as a first-pass filter before human review.
RAG Integration for AI Security
This section is optimized for vector ingestion into an AI-powered architecture assistant. Semantic search enables architects to retrieve relevant ai security guidance through natural language queries.
Note: Reindex the vector store whenever section content is updated to ensure retrieved guidance reflects current standards.
Flowchart
flowchart TD
A([🚀 Start: AI Security]) --> B[Assessment & Discovery]
B --> C{Current State\nDocumented?}
C -->|No| B
C -->|Yes| D[Apply Architecture Principles]
D --> D1[Design for Change]
D --> D2[Least Privilege]
D --> D3[Observability First]
D --> D4[AI Augmentation Readiness]
D1 & D2 & D3 & D4 --> E[Select Design Patterns]
E --> F{NFR Targets\nDefined?}
F -->|No| F1[Define NFRs in nfr/]
F1 --> F
F -->|Yes| G[Document ADRs]
G --> H[Architecture Review Board]
H --> I{Security\nReview Passed?}
I -->|No| I1[Revise Design]
I1 --> H
I -->|Yes| J{ARB\nApproval?}
J -->|Rejected| J1[Address Feedback]
J1 --> H
J -->|Approved| K[Implementation]
K --> L[CI/CD Pipeline]
L --> L1[SAST / DAST Scan]
L --> L2[Architecture Lint]
L --> L3[NFR Validation]
L1 & L2 & L3 --> M{All Gates\nPassed?}
M -->|No| M1[Fix & Rerun]
M1 --> L
M -->|Yes| N[Deploy to Production]
N --> O[Observability Validation]
O --> P[Post-Deployment Review]
P --> Q([✅ Governance Record Closed])
style A fill:#4f8ef7,color:#fff
style Q fill:#10b981,color:#fff
style I1 fill:#fef3c7
style J1 fill:#fef3c7
style M1 fill:#fef3c7
Related Sections
Referenced by
Other substantive pages in the library that link here:
References
- TOGAF 9.2 Architecture Governance Framework — opengroup.org
- COBIT 2019 — isaca.org
- ISO/IEC 42010 — iso.org
- IT Governance — Weill & Ross — Amazon
- Documenting Software Architectures — Bass, Clements, Kazman — Amazon
- Building Evolutionary Architectures — Ford, Parsons, Kua — O'Reilly
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