Enterprise Adoption
A four-phase rollout: baseline one service, add a private policy pack, wire the gate into CI and pull requests, then measure the metrics that matter.
Phase 1 — baseline one service
SecHelix is designed to coexist with the SAST, SCA, DAST, browser, runtime, cloud, and in-house tooling you already run. It coordinates their evidence under one verification standard rather than replacing them.
Choose one representative repository and run in STATIC, then LOCAL if a safe runtime is available. Capture:
- The attack surface and trust boundaries.
- The role by object by action matrix.
- Applicable, unknown, and blocked coverage.
- Verified findings and rejected candidates.
- Security regression tests.
- The release-gate outcome.
- Operator time and manual blockers.
Phase 2 — organization policy pack
Add private requirements without forking the core evidence contract:
- Mandatory authentication and session controls.
- Tenant-isolation invariants.
- Company-specific sensitive-data rules.
- Payment and accounting invariants.
- Approved and forbidden dependencies or providers.
- Cloud and IAM expectations.
- Required log and audit events.
- Production-safe test restrictions.
- Escalation and risk-acceptance owners.
Phase 3 — CI and pull requests
Use SecHelix as an evidence-aware review layer around existing CI. The recommended policy:
- Block on verified regressionsVerified Critical and High security regressions block the release.
- Fail closedMalformed or missing required evidence fails closed to INCOMPLETE.
- Keep uncertainty visibleUNKNOWN and BLOCKED remain visible and cannot silently become NOT_APPLICABLE.
- Own accepted riskAccepted risk requires an owner, a reason, and an expiry or review date.
- Require regression proofSecurity fixes carry regression proof where practical.
Phase 4 — measurement
Track operational metrics rather than raw finding counts.
| Metric | What it tells you |
|---|---|
| Verified precision | Whether reported findings survive scrutiny. |
| False-positive rejection rate | Whether verification is doing work. |
| Detection recall on known fixtures | Whether the workflow finds what it should. |
| Time to verification | How long a candidate stays ambiguous. |
| Time to root-cause fix | Whether repairs land at the boundary or the symptom. |
| Regression-proof rate | Whether fixes are provably closed. |
| Surface classified vs unknown or blocked | How much of the system you can currently assess. |
| Recurrence rate of fixed classes | Whether canonical fixes actually hold. |
Roles
A mature deployment separates these responsibilities:
- Mapper
- Architecture and trust boundaries.
- Specialists
- AuthZ, authN, business logic, injection, supply chain, cloud, AI and MCP.
- Runtime verifier
- Evidence from browser, API, database, and test environments.
- Independent verifier
- Tries to disprove important candidates.
- Owner / release authority
- Accepts risk and makes release decisions.
Do not let model reputation replace independent evidence.
Procurement questions
The seven questions the framework recommends a security review ask, with its own answers:
| Question | Answer |
|---|---|
| Can we inspect and version the full methodology? | Yes. The core is open source under Apache-2.0. |
| Are unsupported scanner or model claims reported as confirmed vulnerabilities? | They should not be. That is the central contract. |
| Can we retain private policy and evidence? | Organization-specific policy can remain private. |
| Can it coexist with existing scanners? | Yes. Adapters normalize evidence rather than requiring replacement. |
| Does it claim measured accuracy? | One blind label-only run is published (precision 0.950). Full-workflow accuracy is NOT_MEASURED. |
| Can it make fail-closed release decisions? | Yes: PASS, PASS_WITH_KNOWN_RISK, BLOCKED, and INCOMPLETE are defined and enforced. |
| Is third-party active testing automatically authorized? | No. Scope and authorization are explicit prerequisites. |
A pilot is useful if it catches a real boundary failure existing review missed, rejects a meaningful false positive that existing automation escalated, finds sibling variants after one root cause, converts a recurring class into a regression test or policy, or produces a clearer release decision than disconnected scanner outputs.