Mipiti — Security posture that maintains itself Describe what you're building. Mipiti generates a threat model, derives security controls, and verifies implementation evidence in your CI pipeline — deterministically, with cryptographic attestation. How it works

Describe & model. You describe the feature. Mipiti produces a structured threat model: assets (tagged with Confidentiality, Integrity, Availability, Usage), capability-defined attackers, trust boundaries, control objectives (the deterministic cross-product of assets × attackers), and concrete implementation controls.

Implement & prove. Your AI coding agent (Claude Code, Cursor, Windsurf, or any MCP-compatible tool) implements the controls and submits typed assertions — structured claims about code properties (e.g. function_exists, test_passes, pattern_absent).

Verify & assure. Your CI runs mipiti-verify. Tier 1 mechanically verifies each assertion (structural checks, no AI). Tier 2 semantically evaluates the code using your configured LLM provider. Results are cryptographically signed and submitted. The platform then deterministically evaluates whether verified controls satisfy each objective — producing a Mitigated / At Risk / Unassessed posture. Risk determination is purely deterministic; no LLM opinions in the final assurance status.

Controls can't drift without detection Verification runs on every commit. When a code change breaks a previously-passing assertion, the control flips to not-verified and its control objective flips to At Risk — targeted to the exact control that regressed, not a vague “something changed.” This is the mechanical heartbeat of the system: security posture tracks the codebase continuously, without waiting for the next audit or pen test. Independent verification of agent-generated code AI coding agents optimize for task completion and can introduce shortcuts unless their work is independently verified — disabled checks, stubbed validation, swallowed exceptions, TODOs that never get done. Mipiti closes that gap: the agent submits evidence, but CI mechanically verifies it against the real codebase. Tier 1 catches structural falsehoods; Tier 2 catches semantic stubs; collective sufficiency catches cherry-picked partial coverage. Failed assertions cannot be overridden by agent reasoning, because the assurance engine evaluates verified evidence rather than natural-language justification. Why this architecture The platform never requires source-code access. Code-touching components (your AI coding agent, your CI pipeline) run in your environment. Mipiti coordinates threat models, evidence metadata, and the deterministic assurance computation. AI helps generate the security framework and evaluate semantic evidence; risk determination itself is purely deterministic. Integrations

MCP server — 69 tools at https://api.mipiti.io/mcp for AI coding agents

REST API — documented at https://api.mipiti.io/docs (OpenAPI schema at /openapi.json)

Jira Cloud — bidirectional integration with OAuth 2.0 (3LO)

Confluence Cloud — automatic page synchronization

CI pipelines — GitHub Actions, GitLab CI, or any CI via pip install mipiti-verify

Compliance frameworks (built-in) OWASP ASVS 5.0, ISO 27001:2022, SOC 2 TSC, NIST CSF 2.0, GDPR, FedRAMP Rev 5, PCI DSS 4.0.1, EU CRA 2024/2847. Custom frameworks can be imported as CSV or JSON.