Defensive AI Systems Builder focused on secure remediation workflows, human-reviewed patch validation, scoped threat modeling, and audit-ready evidence.
- local-first defensive AI workflow
- scoped execution
- human approval
- static frontend security review
- safe rendering remediation
- patch validation discipline
- public sanitized evidence
- audit-ready documentation
This repository demonstrates the ability to turn AI-assisted engineering into a governed defensive workflow that reviewers can inspect.
The value is not an offensive finding. The value is the operating pattern:
- define the authorized scope
- inventory the system before changing it
- model realistic trust boundaries
- separate real risks from noise
- preserve a small, reviewable remediation
- validate locally
- document evidence without leaking private data
- keep humans in approval points
That is the practical bridge between AI-assisted engineering and trustworthy defensive execution.
This public sanitized lab is aligned with Daybreak concepts and defensive AI remediation workflows.
| Capability | Repository evidence |
|---|---|
| Finding and prioritizing realistic risks | docs/evidence/02_security_triage.md records rendering regression, evidence drift, validation, deployment hardening, public narrative, and secret hygiene risks. |
| Generating safe remediation | docs/remediation/remediation_plan.md separates applied low-risk documentation corrections from deferred release-affecting actions. |
| Validating fixes | docs/validation/validation_report.md records remote review findings and local PowerShell validation results. |
| Producing audit-ready evidence | docs/evidence/01_repository_inventory.md, docs/threat-model/threat_model.md, and this summary create a traceable evidence trail. |
| Keeping humans in the review loop | docs/remediation/human_approval_checklist.md defines gates for scope, sanitization, code behavior, documentation, and merge preparation. |
No affiliation with OpenAI is claimed. No acceptance, access, or authorization from OpenAI is implied.
README.md: public positioning, actual system scope, defensive scope, validation commands, and human review gate.SECURITY.md: defensive-only security policy and reporting rules.AGENTS.md: local defensive AI operating rules.app.js: remediated text rendering behavior.docs/evidence/01_repository_inventory.md: repository structure, technologies, entry points, dependencies, risk surface, and out-of-scope boundaries.docs/threat-model/threat_model.md: sanitized defensive threat model for the static local-first app and public evidence workflow.docs/evidence/02_security_triage.md: prioritized risk table with impact, probability, severity, evidence, recommended remediation, and status.docs/remediation/remediation_plan.md: safe remediation strategy, applied changes, deferred actions, and next remediation steps.docs/remediation/human_approval_checklist.md: review gates for scope, sanitization, code behavior, documentation, and commits.docs/validation/validation_report.md: remote review findings, local validation results, corrections made, and pending items.
- this is a public sanitized lab
- not a production vulnerability scan
- not an offensive security project
- not a replacement for enterprise-grade security review
- not evidence of OpenAI affiliation, access, or acceptance
- not a guarantee of complete security coverage
- keep CI validation aligned with repository evidence and branch protection
- add reviewed deployment headers if publicly hosted
- add signed release notes after merge