AI agents are now operating inside enterprise environments—reading emails, querying databases, making decisions, triggering workflows. Established security architecture frameworks remain valid but require extension to accommodate AI agents as a new tenant category.
Three Foundational Frameworks
SABSA
Business-driven, risk-derived. Provides the "why" through traceability from business requirements to controls. Six layers from contextual to component.
TOGAF
Ensures security aligns with enterprise architecture lifecycle. Provides the "where" in enterprise context: application portfolios, migration, governance.
OSA
Technical control patterns and reference architectures. Delivers the "how": perimeter security, identity federation, data protection.
AI Agent-Specific Changes
1. Trust Boundaries Transformation
Agents traverse multiple zones within single task execution at speed and breadth exceeding traditional user patterns. Requires cross-zone traversal models and cumulative access monitoring.
2. Non-Human Identity (NHI) Governance
New identity category requiring lifecycle management, rotation, task-scoped least-privilege enforcement, behavioural monitoring, and attestation of agent actions.
3. Data Flow Architecture
Agents create derived sensitive data through multi-source queries. Data exfiltration through inference API calls. Classification needed at agent access point, not just at rest/transit.
4. Decision Authority Framework
Explicit mapping of autonomous vs. human-approval decisions across: read-only retrieval, low-risk modification, high-risk operations, access provisioning, financial transactions.
Seven Key Deliverables
- Agent inventory and cataloguing
- Trust zone mapping for agent traversal
- NHI governance framework
- Data classification for agent access
- Prompt injection defence patterns
- Human-in-the-loop decision framework
- Comprehensive audit logging for autonomous actions
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