Secure Context Value Model

Why this page exists. SecurityRecipes should not look like a pile of interesting artifacts. It should look like a product that can become a hosted secure context layer. The value model explains the open moat, the paid wedges, the buyer segments, and the conservative ROI assumptions in one MCP-readable artifact.

SecurityRecipes is positioned as The Secure Context Layer for Agentic AI. The technical foundation is already in place: secure context packs, MCP authorization and drift controls, run receipts, telemetry contracts, evals, incident response, entitlement review, protocol conformance, and a trust-center export. The next acquisition-grade move is to make the business case explicit.

The Secure Context Value Model does that without pretending the public repo has already proven hosted revenue. It names what is proven now, what remains assumption-based, which buyer segments care, which paid product wedges are natural, and what customer telemetry must be attached before the ROI story becomes sales-grade.

What was added

data/assurance/secure-context-value-model-profile.json - source profile for buyer segments, value drivers, adoption scenarios, monetization wedges, diligence questions, and source references.
scripts/generate_secure_context_value_model.py - deterministic generator and --check validator.
data/evidence/secure-context-value-model.json - generated value model with scenario economics, evidence hashes, source-pack status, and acquisition readiness.
recipes_secure_context_value_model - MCP tool for the full model, a value driver, buyer segment, scenario, monetization wedge, or diligence question.

Run it from the repo root:

python3 scripts/generate_secure_context_value_model.py
python3 scripts/generate_secure_context_value_model.py --check

What the model contains

Section	Purpose
`value_model_summary`	Source-pack readiness, scenario count, value-driver count, assumption status, and annual net value range from the default scenarios.
`value_drivers`	Open knowledge distribution, production MCP control plane, trust-center evidence, runtime receipts/evals, and standards drift.
`buyer_segments`	Frontier model lab, AI platform vendor, security platform vendor, and regulated enterprise buyer views.
`adoption_scenarios`	Conservative pilot, platform rollout, and hosted MCP control-plane economics.
`monetization_wedges`	Hosted MCP policy, private secure-context registry, run-receipt vault, trust-center API, and continuous agentic evals.
`diligence_questions`	Answers to why this is not docs-only, what is open, what is paid, what proves ROI, and what remains unproven.
`acquisition_readiness`	Current signal, missing proof points, and the conditions needed before a $10-20M outcome is credible.

The ROI math is intentionally conservative and explicit. It uses assumptions such as runs per month, avoided remediation hours, reviewer time, loaded hourly cost, platform cost, and implementation cost. The generated pack labels those numbers as assumptions until customer run receipts and telemetry replace them.

Product implications

This feature pushes the site toward the right shape for a serious enterprise or acquirer review:

The open corpus remains the distribution engine.
The generated evidence packs become the product proof.
The MCP server becomes the inspectable access layer.
Hosted MCP policy, private context, drift monitoring, receipts, eval replay, and trust-center APIs become the paid surface.
Customer telemetry becomes the proof point for ROI and renewal.

That is a more credible path than selling prompts. A buyer can inspect the technical artifacts, understand the economic assumptions, and see exactly what still needs to be built for hosted enterprise value.

MCP examples

Inspect the full model:

recipes_secure_context_value_model()

Inspect one value driver:

recipes_secure_context_value_model(driver_id="production-mcp-control-plane")

Inspect the hosted MCP scenario:

recipes_secure_context_value_model(scenario_id="hosted-mcp-control-plane")

Answer a diligence question:

recipes_secure_context_value_model(question_id="what-is-acquirable")

Industry alignment

The profile is source-backed by current primary guidance:

MCP 2025-11-25 key changes for protocol drift, incremental scope consent, URL elicitation, and task support.
MCP Authorization for resource indicators, audience validation, PKCE, and token-passthrough denial.
NIST AI Agent Standards Initiative for interoperable protocols, agent identity, and security evaluations.
CAISI AI Agent Security RFI for indirect prompt injection, data poisoning, misaligned actions, and deployment access controls.
OWASP Top 10 for Agentic Applications 2026 for agent behavior, tool, identity, context, inter-agent, and rogue agent risks.
CISA Secure by Design for secure defaults, executive ownership, transparency, and measurable customer outcomes.