Security
Last updated: 2025-05-12
This document outlines the security considerations and best practices for implementing multi-agent systems in enterprise environments. The architecture prioritizes responsible AI usage, data protection, access control, and observability at every layer.
Core Security Principles
Identity Enforcement
- Agents and orchestrators authenticate via Azure AD or equivalent identity providers.
- Role-based access control (RBAC) governs agent execution and orchestration permissions.
- Ensure mutual authentication between agents using enterprise identity systems (Entra ID, SPIFFE, etc.).
- Use X.509 certificates or JWTs signed by an internal CA to verify agent provenance.
- Support rotating credentials for long-lived agents.
Scoped Agent Capabilities
- Agent registry enforces capability declarations and metadata tagging.
- Only approved capabilities can be executed in runtime.
Data Governance & Storage Policies
- All storage layers (state, conversation history, registry) support encryption-at-rest.
- Retention policies are configurable per agent or orchestrator.
Secure Communication
- All communication between agents, orchestrator, and external tools is encrypted via HTTPS or mutual TLS.
- MCP protocol supports signed payloads and call tracing.
Policy-Controlled Tool Invocation
- MCP server validates tool access policies before invocation.
- Integration layer enforces audit logging for all tool calls.
Memory Management and Redaction
- Short-term memory is scoped to current conversation thread.
- Long-term memory undergoes PII redaction and consent-based storage.
LLM Usage & Guardrails
- Prompt templates include safety instructions.
- Responses are filtered by safety classifiers and optionally passed through moderation APIs.
Auditability and Observability
- Every orchestration and agent call is logged with metadata: timestamp, caller identity, input hash, output hash.
- Logs are shipped to centralized observability platform (e.g., Azure Monitor or OpenTelemetry).
Adversarial Testing & Red Teaming
- Simulate attacks (prompt injection, message corruption, impersonation).
- Use chaos security engineering to validate robustness of inter-agent communication.
Agent Versioning and Rollback Strategy
- Version every agent’s logic, prompt configuration, and communication contract (e.g., using SemVer).
- Support graceful coexistence of multiple versions (e.g., v1 agents in production, v2 agents in testing).
- Use feature flags or routing rules to selectively enable agent versions per customer or region.
- Always enable immediate rollback to a previous version if anomalies are detected (e.g., via GitOps or CI/CD workflows).
Business Continuity and User Safety
- Establish SLOs for agent performance (latency, availability, decision quality).
- Define user impact metrics before deployment (e.g., error rate, abandonment rate, CSAT).
- Automate runtime health checks and user experience monitoring tied to release gates.
- Include manual override mechanisms (e.g., pause/disable an agent class) without affecting the overall system.
Enterprise Data Protection in RAG Scenario
Document Indexing Layer
When indexing documents, ensure to include sensitivity and/or permissions (owner, edittable, etc) information in included as a retrivable field.
Retrival Layer
When retriving/vector search for documents, filter on relevant sensitivity or permissions based on agent or user role or identity
Agent (using RAG) Layer
Before generating response from the resulting document, ensure the retrieved documents are viewable by the user as another layer of protection
Indentity Layer
Ensure that the user identity has roles or permissions associated and the identity is secured by following the "Identity Enforcement" section of this guide
Compliance Considerations
- Support for GDPR/CCPA consent flows.
- Optional on-premise deployment for sensitive workloads.
- Integration with enterprise data loss prevention (DLP) systems.
Future Additions
- Integration with confidential computing enclaves (TEE).
- Fine-grained token scopes for agents.
- Agent-level anomaly detection models.
For implementation details and threat modeling scenarios, refer to the Threat Model Worksheet.