AI/ML / Multi Agent Refarch

Multi-Agent Reference Architecture Controls

Version: DEV

ID	Title	Objective	Control Family	Threat Mappings	Guideline Mappings	Assessment Requirements
CCC.MARefArc.CN01	Data Filtering From External Knowledge Bases	Sanitize, filter, and classify data ingested by the Knowledge Layer from internal and external source bases before it is embedded into the vector store or used for retrieval-augmented generation, preventing inadvertent exposure or manipulation of sensitive organizational knowledge.	PREV	4	1	2
CCC.MARefArc.CN02	User, Application, and Model Firewalling	Establish enforced trust boundaries between the user, the application, and the models and tools by routing all traffic through the agent, LLM, and MCP gateways where guardrails inspect and constrain requests and responses.	PREV	8	1	2
CCC.MARefArc.CN03	System Acceptance Testing	Validate agents, models, and end-to-end workflows against accuracy, robustness, bias, drift, and compliance criteria before promotion to production, and re-validate after material changes.	PREV	6	1	2
CCC.MARefArc.CN04	Data Quality and Classification	Assess the quality of, and assign classification and sensitivity labels to, all data used for grounding, training, and fine-tuning, and enforce handling rules derived from those labels throughout the Knowledge and LLM layers.	PREV	12	1	2
CCC.MARefArc.CN05	Legal and Contractual Frameworks for AI Systems	Establish contractual controls with model and MCP service providers covering data handling, retention and deletion, intellectual property, liability, and supply-chain integrity.	PREV	7	1	2
CCC.MARefArc.CN06	Quality of Service and DDoS Prevention	Protect model and tool availability by enforcing quality-of-service controls, rate limits, and abuse and DDoS mitigation at the gateways.	PREV	3	1	2
CCC.MARefArc.CN07	AI Model Version Pinning	Pin and record explicit model versions in the Model Registry so that model behaviour is reproducible and provider-side changes are surfaced rather than silently absorbed.	PREV	2	1	2
CCC.MARefArc.CN08	Role-Based Access Control for AI Data	Enforce least-privilege, role-based access control over all AI data stores, including source bases, the vector store, and model artifacts.	PREV	6	1	2
CCC.MARefArc.CN09	Encryption of AI Data at Rest	Encrypt AI data at rest, including the vector store and source repositories, so that storage-level access does not expose embeddings or sensitive content.	PREV	4	1	2
CCC.MARefArc.CN10	AI Firewall Implementation and Management	Implement and operate an AI firewall within the guardrail components that inspects prompts, content, and responses for injection, sensitive data, and policy violations.	PREV	8	1	2
CCC.MARefArc.CN11	Agent Authority Least Privilege Framework	Constrain each agent's authority to the minimum set of tools, APIs, and data required for its task, enforced by the runtime and MCP guardrails, and prevent permission creep during operation.	PREV	2	1	2
CCC.MARefArc.CN12	Tool Chain Validation and Sanitization	Validate tool selection, sanitize tool-call parameters, and constrain tool sequencing within the runtime and MCP guardrails to prevent manipulation of agent tool use.	PREV	5	1	2
CCC.MARefArc.CN13	MCP Server Security Governance	Govern the onboarding, verification, and ongoing monitoring of MCP servers so that only approved, integrity-verified servers are reachable, and supply-chain compromise is detected.	PREV	5	1	2
CCC.MARefArc.CN14	Multi-Agent Isolation and Segmentation	Isolate agents and their memory and state so that compromise or failure of one agent cannot propagate to others, and enforce segmentation of agent-to-agent communication.	PREV	3	1	2
CCC.MARefArc.CN15	Agentic System Credential Protection Framework	Prevent agents from discovering, extracting, or misusing credentials by brokering secrets outside agent-accessible surfaces and constraining tool access to credential stores.	PREV	3	1	2
CCC.MARefArc.CN16	AI Data Leakage Prevention and Detection	Detect leakage of sensitive data in model inputs and outputs and in telemetry, and alert and respond when disclosure is detected.	DET	2	1	2
CCC.MARefArc.CN17	AI System Observability	Instrument every layer to emit logs, traces, metrics, and events to the Observability Layer so that behaviour, drift, availability, and data handling are continuously visible and auditable.	DET	10	1	2
CCC.MARefArc.CN18	AI System Alerting and Denial of Wallet Monitoring	Monitor spend and usage of models and tools, and alert on anomalous consumption indicative of Denial of Wallet or runaway agentic loops.	DET	3	1	2
CCC.MARefArc.CN19	Human Feedback Loop for AI Systems	Capture human feedback on agent outputs through the Feedback Engine and Human Supervision capabilities and feed it into evaluation and improvement of agents and models.	DET	5	1	2
CCC.MARefArc.CN20	Citations and Source Traceability for AI-Generated Information	Attach citations and source traceability to AI-generated information so that outputs can be verified against retrieved sources and decisions can be explained.	DET	4	1	2
CCC.MARefArc.CN21	Automated Evaluation Using LLM-as-a-Judge	Use automated model-based evaluation in the Evaluation Layer to assess output quality, grounding, bias, and policy compliance at scale.	DET	8	1	2
CCC.MARefArc.CN22	Preserving Source Data Access Controls in AI Systems	Propagate the access controls of source data into the retrieval path so that retrieval and generation cannot expose content a requesting user is not authorized to see.	DET	4	1	2
CCC.MARefArc.CN23	Agent Decision Audit and Explainability	Record an auditable trace of agent decisions, including tool selections, inputs, and rationale, sufficient to explain and review autonomous actions after the fact.	DET	3	1	2