Architectural truth
How provenance, identity, relationships, history, and gaps create a reliable system model.
Resources / Working knowledge
Build the review practice, not just the report.
Practical material for teams turning scattered technical evidence into decisions they can explain.
Content system
Four questions guide everything we publish.
The library follows the same reasoning path as the product: establish truth, understand consequence, constrain AI, and improve engineering practice.
Security consequence
How findings become paths, blast radius, ownership, and actionable decisions.
Bounded AI systems
How agents, model providers, tools, context, retention, and cost remain governable.
Engineering practice
How to introduce continuous review without disrupting the way teams build and ship.
Guided paths
Start with your role and the decision in front of you.
Choose a working path through architecture truth, structural risk, agent boundaries, and implementation practice. Each path ends with an outcome your team can apply.
Choose your review question
A path through the library, not another content pile.
Security lead path
Which structural risks deserve scarce review time?
01Benchmark the reviewDefine evidence, isolation, and decision quality before comparing agents.02Build the context engineConnect findings to ownership, reachability, trust, and change.03Trace blast radiusPrioritise using independently supported architecture paths.
Expected outcome
Leave with an evaluation contract, a context model, and a path-based prioritisation method.
Library
Start with the decision in front of you.
Filter by format or search by the question your team needs to answer.
Guide
Publish trust claims with evidence
Connect privacy and security language to implemented controls, known boundaries, upstream tools, and honest release gates.
Security and legal teamsOpen
Guide
Benchmark an AI security review
Evaluate evidence coverage, citation validity, replayability, isolation, decision quality, and measured cost instead of grading polished prose.
Security leadersOpen
Field note
Architecture intelligence vs scanners
Understand where detection products stop and where cross-source architecture context begins, without forcing a replacement decision.
AppSec teamsOpen
Guide
Governed model routing
Assign explicit model roles while keeping evidence access, retention, cost, and action authority consistent across providers.
AI platform teamsOpen
Guide
MCP as an evidence gateway
Design MCP around scoped evidence submission and typed actions rather than ambient repository or shell access.
Platform engineersOpen
Reference
Continuous review checklist
A practical operating rhythm for source freshness, coverage, review gates, remediation, and architecture memory.
Engineering teamsOpen
Guide
Evidence-backed architecture
Build an architectural model where every material relationship can explain its source, confidence, and history.
Security architectsOpen
Field note
The security context engine
Connect isolated findings to ownership, exposure, reachable paths, and system consequence.
Security engineersOpen
Guide
AI agent boundaries
A practical model for provenance, tools, human approval, telemetry, and cost controls.
AI architectsOpen
Reference
MCP evidence gateway
See how approved tools submit tenant-scoped evidence without turning repository text into instructions.
Platform teamsOpen
Reference
Security review benchmark
Read the open evaluation contract for evidence coverage, citation accuracy, replayability, and decision quality.
Security leadersOpen
Interactive
Explore a living review
Use the guided product environment to inspect evidence, agent artifacts, architecture, and measured cost.
Evaluation teamsOpen
Field note
Trust boundaries and blast radius
Trace how identity, delivery, data, and runtime relationships change the consequence of a security weakness.
Threat modelersOpen
Bring the next architecture decision into focus.
Show us the sources, constraints, and review question. We will map a safe evaluation path before you connect production systems.