Amazon AI Safety
Major cybersecurity platform expanding into AI security via acquisitions (Protect AI for $500M+).
Score
50.0 / 100
Evidence
6 items
Confidence
medium
Strong safety posture with established governance frameworks and active risk management.
Strengths:Governance Maturity, Technical Safety, External Engagement
Weaknesses:Risk Assessment, Regulatory Readiness
Focus Areas
cybersecurityAI securitynetwork securityacquired Protect AI
Safety Profile
Table of Contents
Security Assessment
Security-relevant indicators for vendor evaluation
Security Posture
50
TS-01dim: 55
Red Teaming & Pre-deployment Testing
Adversarial testing before deployment
TS-05dim: 55
Robustness & Adversarial Resilience
Resistance to adversarial attacks
RA-01dim: 45
Sector-Specific Risk Assessment
Risk analysis for deployment context
RA-03dim: 45
Dual-Use & Misuse Risk
Dangerous capability awareness
RA-07dim: 45
Incident History & Track Record
Past incidents and response quality
EE-04dim: 50
Vulnerability Disclosure Program
Bug bounty or CVE reporting process
Incident History
Amazon AI Safety 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 Maturitypreliminary
Published policies, corporate structure, safety mandate, whistleblowing, executive commitment.TS
Technical Safetypreliminary
Benchmarks, adversarial robustness, fine-tuning safety, watermarking, model cards, research output.RA
Risk Assessmentpreliminary
Dangerous capability evaluations, thresholds, external testing, bug bounty, halt conditions.RR
Regulatory Readinesspreliminary
ISO 42001, EU AI Act compliance, GPAI obligations, international commitments, incident reporting.EE
External Engagementpreliminary
Survey participation, research support, transparency, behavior specs, open-source contributions.Data Sources & Methodology
Scoring methodology v0.1 · 40 indicators · 6 frameworks
Last assessment: 2026-03-23 · Confidence: medium · Evidence: 6 items
NIST AI RMF · EU AI Act · ISO 42001 · FLI AI Safety Index · MLCommons AILuminate · METR
Scores reflect publicly available information. A low score may indicate limited transparency rather than poor safety practices.