Your AI
is making decisions.
Can you defend them?
HAIEC is the AI exposure control layer for teams that use AI in hiring, operations, and decision-making. Deterministic scanning. Evidence-grade artifacts. No AI testing AI.
AI Governance Maturity
Moderate exposure · 3 critical gaps
- No bias audit on file — NYC LL144 violation risk
- AI endpoint unprotected against prompt injection
- Model drift unmonitored since deployment
- Incomplete evidence trail for SOC 2 review
- SARIF output + GitHub integration active
Free · No signup · Results in minutes
Most teams are running AI blind.
- 01AI is screening candidates. There is no bias audit. NYC LL144 has been enforced since July 2023.
- 02AI is drafting regulated output. There is no hallucination detection layer. No source provenance logging.
- 03AI endpoints are live in production. They have never been tested against prompt injection, jailbreaks, or data exfiltration.
- 04The model was validated at deployment. It has not been re-validated since. The input population has changed.
- 05The board has disclosed material reliance on AI. No one has reviewed the governance infrastructure that would need to back that disclosure.
"If a regulator, auditor, or board member asked tomorrow — show me how this system made that decision — could you?"
The gap between what you've deployed and what you can prove is your current liability surface.
Not a dashboard.
An evidence layer.
HAIEC is a deterministic AI governance layer that provides continuous exposure monitoring, evidence-grade logging, and audit-ready artifact generation. Every output is reproducible, signed, and mapped to a specific regulatory standard.
Static AI Security Analysis
80+ proprietary rules across authentication, prompt injection, tool abuse, RAG poisoning, and tenant isolation. SARIF 2.1.0 output. GitHub-native.
Runtime Adversarial Testing
268+ adversarial payloads against live AI endpoints. Tests for jailbreaks, system prompt extraction, data exfiltration, and instruction override.
Audit-Grade Evidence Generation
SHA-256 signed artifacts. Cryptographic audit trails. Immutable evidence packages mapped to 9 compliance frameworks. Reproducible, not probabilistic.
Two paths.
One standard.
Continuous risk command for operators who run their own stack. Formal attestation for teams that need signed, board-defensible proof. These are not substitutes — they are layers.
AI Risk Command
Run your own AI exposure control stack.
Infrastructure-grade continuous governance for teams that want control, not commentary. Security scanning, bias indicators, drift monitoring, and evidence generation — running on your schedule, mapped to your frameworks.
- AI Exposure Score + continuous monitoring
- 80+ static security rules · SARIF export
- 268+ adversarial runtime payloads
- Bias risk indicators across protected classes
- Governance artifact generation (SOC 2, ISO, NIST)
- Regulatory heat map · NYC LL144 · Colorado · EU AI Act
- GitHub App integration · CI/CD pipeline hooks
- Kill Switch SDK · 5-layer defense system
Built for — CTO · CISO · Security Lead · Technical Founder
Activate AI Risk CommandAI Defensibility Audit
Board-defensible. Regulator-ready. Signed.
A formal attestation event. Not a software subscription — a signed, documented, legally reviewable review of your AI systems, delivered as an audit-grade evidence package.
- Bias impact statistical analysis (NYC LL144, ECOA)
- Decision process trace validation
- Model documentation review and gap analysis
- SHA-256 signed artifact verification
- Legal-ready documentation package
- Executive summary for board disclosure
- Regulatory framework attestation letter
- Quarterly cohort — limited onboarding per cycle
Built for — HR Leaders · Compliance Officers · General Counsel · Executive Teams
Request Assurance ReviewDashboards are not evidence.
What most tools give you
- ✕Checklists and self-attestations
- ✕Generic compliance reports
- ✕AI-assessed AI — probabilistic, non-reproducible
- ✕Screenshots and PDF exports
- ✕Policy templates without evidence binding
- ✕Frameworks mapped to your answers, not your code
What HAIEC generates
- ✓Deterministic scanning — 100% reproducible outputs
- ✓SHA-256 signed, timestamped artifact packages
- ✓Cryptographic audit trails for every finding
- ✓Regulatory citations mapped to specific code evidence
- ✓Bias analysis with statistical justification (four-fifths rule)
- ✓Evidence that survives legal discovery and regulator review
Core Principle
"Stop testing AI with AI."
Using an AI system to evaluate an AI system produces probabilistic assessments of probabilistic behavior. A regulator does not accept this as evidence. HAIEC's deterministic engines produce outputs that are reproducible, auditable, and legally defensible — because they were not generated by the system being evaluated.
Where your risk lives right now.
GitHub & Repository Metadata
What your codebase signals before anyone reads it. AI model references, compliance gaps, dependency exposure, missing governance files.
Learn moreStatic Code Analysis
80+ rules across prompt injection, missing auth, tool abuse, RAG poisoning, tenant isolation. Provable data-flow paths. Not heuristics.
Learn moreDynamic Runtime Testing
268+ adversarial payloads against your live endpoints. Tests what your code cannot show — how the model responds under attack conditions.
Learn moreIf any of these apply,
the exposure is current — not future.
You use AI in hiring or promotion decisions
NYC Local Law 144 requires annual bias audits. A December 2025 State Comptroller audit found 17 potential violations where the city found only 1.
Your AI makes or influences consequential decisions
Credit scoring, benefits eligibility, risk assessment, clinical recommendation. If the output affects someone's options, regulators will eventually ask how it was validated.
You've disclosed AI use to investors or customers
SEC guidance, enterprise security reviews, and board-level D&O exposure all follow from disclosure. The gap between what you've disclosed and what you can prove is a governance liability.
You're preparing for SOC 2, ISO 27001, or an enterprise contract
Enterprise security questionnaires increasingly include AI-specific controls. Reviewers ask about model documentation, bias testing, and AI incident response.
Your AI system runs in a regulated industry
Healthcare, financial services, insurance, government contracting. The same pattern repeats: AI deployed faster than governance. A letter arrives. There is nothing to produce.
You're raising your next funding round
Series B and beyond, due diligence now includes AI governance review. Sophisticated investors — and their lawyers — are asking questions that weren't being asked 18 months ago.
AI failures don't fail quietly.
The cost of an AI governance failure is rarely the fine. It's what the fine makes visible — the absence of controls that should have existed, the documentation that wasn't generated, the evidence that can't be produced.
Find Your Gaps Before They Do- Regulatory investigation triggered by external complaint or audit
- Legal discovery requests evidence that was never generated
- Media narrative: "AI system found to discriminate"
- Enterprise customer pause or cancellation pending security review
- Board inquiry: who is responsible, what controls exist?
- Six-to-nine month remediation program begins after the fact
- Remediation cost exceeds the value the system generated
Evidence mapped to the
standard that matters to you.
Patent-pending architecture. Deterministic evidence generation. Static + runtime AI validation engines. Continuous governance, not one-time paperwork. Built for teams that expect scrutiny.
The Only Question That Matters
AI is operational
infrastructure now.
Defend it like it is.
If your AI is influencing outcomes, it must withstand examination. The gap between what you've deployed and what you can prove closes in one of two ways: you close it, or someone else discovers it.