Ecosystem/Gray Swan AI

Gray Swan AI

Seed

Builds adversarial testing tools that probe AI models for safety vulnerabilities - finding ways models can be manipulated before attackers do. CMU-affiliated. Halcyon portfolio.

HQUS
Est2024
grayswan.ai
Score
66.0 / 100
Confidence
Evidence-based

Strong safety posture with established governance frameworks and active risk management.

Strengths:Governance Maturity, Technical Safety, Risk Assessment, External Engagement
Weaknesses:Regulatory Readiness
Competitive positioning

One of few companies focused on adversarial AI testing with a safety-first mission. Competes with Haize Labs (similar stage) and Patronus AI (larger). Differentiates through CMU adversarial research background.

Key risk

Enterprise buyers don't yet have 'AI red-teaming' as a standard procurement category. Most demand from frontier labs and government.

Enterprise traction

Government and lab contracts visible. Enterprise customers not confirmed.

governmentfrontier labs
Safety area

Evaluations & Benchmarking

Enterprise business needs
Test my AI before deployment

Security Assessment

Security-relevant indicators for vendor evaluation

Security Posture
75
TS-01dim: 78
Red Teaming & Pre-deployment Testing
Adversarial testing before deployment
TS-05dim: 78
Robustness & Adversarial Resilience
Resistance to adversarial attacks
RA-01dim: 72
Sector-Specific Risk Assessment
Risk analysis for deployment context
RA-03dim: 72
Dual-Use & Misuse Risk
Dangerous capability awareness
RA-07dim: 72
Incident History & Track Record
Past incidents and response quality
EE-04dim: 80
Vulnerability Disclosure Program
Bug bounty or CVE reporting process
Incident History
Gray Swan AI incident records sourced from AIAAIC Repository and public reporting.
Integration: AIAAIC, OECD AI Incidents Monitor
Third-Party Audits
External audit reports, SOC 2 attestations, and ISO certifications verified where published.
Sources: Company filings, registry lookups
CVE & Disclosures
Known vulnerabilities and security advisories from NVD, GitHub Security Advisories, and vendor pages.
Sources: NVD, GHSA, vendor disclosure pages

Dimension Breakdown

GM
Governance Maturityevidence
Published policies, corporate structure, safety mandate, whistleblowing, executive commitment.
55
TS
Technical Safetyevidence
Benchmarks, adversarial robustness, fine-tuning safety, watermarking, model cards, research output.
78
RA
Risk Assessmentevidence
Dangerous capability evaluations, thresholds, external testing, bug bounty, halt conditions.
72
RR
Regulatory Readinessevidence
ISO 42001, EU AI Act compliance, GPAI obligations, international commitments, incident reporting.
45
EE
External Engagementevidence
Survey participation, research support, transparency, behavior specs, open-source contributions.
80

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Scoring methodology v0.1 - View full rubric