Agentic Control Plane Blueprint

What this is. The blueprint is the executive and architecture layer above the generated packs. It explains how SecurityRecipes becomes a credible secure context layer for agentic AI, not just a documentation site or prompt library.

SecurityRecipes already has the hard parts of an enterprise agentic program: secure context trust, MCP gateway policy, authorization conformance, connector trust, non-human identity, memory boundaries, skill supply-chain checks, egress decisions, run receipts, red-team drills, readiness scoring, and measurement probes.

The Agentic Control Plane Blueprint turns those individual artifacts into one product story a buyer, AI platform owner, security architect, GRC reviewer, or acquirer can evaluate quickly:

What is the reference architecture?
Which generated packs prove each layer?
Which MCP tools expose the evidence to agents and review portals?
Which current industry references does the architecture track?
What is the path from open knowledge to production MCP revenue?

Generated artifact

Source model: data/assurance/agentic-control-plane-blueprint.json
Generator: scripts/generate_agentic_control_plane_blueprint.py
Evidence pack: data/evidence/agentic-control-plane-blueprint.json
MCP tool: recipes_agentic_control_plane_blueprint

Regenerate and validate the pack:

python3 scripts/generate_agentic_control_plane_blueprint.py
python3 scripts/generate_agentic_control_plane_blueprint.py --check

Why this matters

Agentic security is moving from “can we prompt the model safely?” to “can we govern the action layer?” NIST’s 2026 AI Agent Standards Initiative emphasizes interoperable standards, community protocols, agent authentication and identity, and security evaluations. OWASP’s Agentic Top 10 frames the new risk surface around goal hijack, tool misuse, identity abuse, agentic supply chain, memory and context poisoning, cascading failures, and rogue-agent behavior. The current MCP authorization specification adds concrete requirements around resource indicators, token audience validation, PKCE, HTTPS, client metadata, and forbidden token passthrough.

That is exactly where SecurityRecipes should sit: between agents and the systems they want to use, making context and authority understandable, queryable, and enforceable.

Blueprint layers

Layer	What it proves	Core evidence
Workflow scope and default-deny control	Agents only run declared workflows and undeclared tool calls fail closed.	Workflow manifest, MCP gateway policy, assurance pack, readiness scorecard
Secure context provenance	Returned context has owner, trust tier, source hash, citation rule, poisoning scan, and workflow package hash.	Secure Context Trust Pack, Context Poisoning Guard, Agentic System BOM, Measurement Probes
MCP authorization and connector trust	Remote MCP servers are reviewed for token audience, resource binding, PKCE, scope drift, and connector trust.	Connector Trust Pack, Connector Intake Pack, Authorization Conformance, Gateway Policy
Agent identity and delegation	Every agent run has owner, delegated scope, explicit denies, review linkage, and revocation expectations.	Agent Identity Ledger, Gateway Policy, Run Receipt Pack, Capability Risk Register
Memory, skill, and runtime boundaries	Skills, rules files, hooks, vector memory, and persistent memory cannot silently inherit authority.	Memory Boundary Pack, Skill Supply-Chain Pack, Poisoning Guard, Red-Team Drills
Context egress and data boundaries	Context does not leave tenant, model, telemetry, MCP, or public-corpus boundaries without policy.	Context Egress Boundary, Secure Context Trust Pack, Run Receipts, Assurance Pack
Evidence receipts and Agentic System BOM	Runs can be reconstructed from context, tools, policy decisions, approvals, verifiers, closure, and revocation.	Run Receipt Pack, Agentic System BOM, Assurance Pack, Measurement Probes
Measurement, red-team replay, and threat alignment	Current threat signals become probes, drills, readiness decisions, and roadmap actions.	Threat Radar, Measurement Probe Pack, Red-Team Drill Pack, Readiness Scorecard

Buyer diligence questions

Use the generated buyer_due_diligence_matrix when a customer or acquirer asks for evidence:

Question	Evidence path
Which context sources are allowed into an agent run, and how is source drift detected?	`recipes_secure_context_trust_pack`, `recipes_context_poisoning_guard_pack`
How does the product prevent token passthrough, wrong-audience tokens, scope creep, and unreviewed MCP tools?	`recipes_mcp_authorization_conformance_pack`, `recipes_mcp_connector_trust_pack`
Who owns an agent identity, what can it do, and how is one unsafe run revoked?	`recipes_agent_identity_ledger`, `recipes_agentic_run_receipt_pack`
Can the team reconstruct the exact policy, context, tool, approval, verifier, and egress path after an incident?	`recipes_agentic_run_receipt_pack`, `recipes_agentic_system_bom`
Which workflows are ready to scale, which stay in a guarded pilot, and which are blocked?	`recipes_agentic_readiness_scorecard`, `recipes_agentic_measurement_probe_pack`

Product strategy

The open project should stay useful and forkable. That is the adoption engine. The commercial value sits above it:

Stage	Product surface
Open foundation	MIT-licensed recipes, generated evidence packs, read-only MCP server, deterministic policy evaluators.
Production MCP server	Hosted secure-context retrieval, context signing, MCP authorization conformance, connector trust monitoring, and run receipt storage.
Enterprise expansion	Tenant evidence ingestion, identity-provider adapters, hosted red-team replay, measurement probes, and trust-center exports.
Strategic acquisition fit	Frontier labs, AI coding platforms, cloud platforms, and security vendors need a credible control layer for agentic tool use and context.

MCP examples

Get the full architecture summary:

{}

Get one blueprint layer:

{
  "layer_id": "mcp_authorization_and_connector_trust"
}

Get buyer evidence for a diligence question:

{
  "question_id": "runtime-evidence"
}

Get only layers that need evidence attention:

{
  "status": "needs_attention"
}

Source anchors

The source model should be reviewed when these references change: