PARTNERS

Add selected Workbench capabilities through bounded OEM and partner integrations

← AttestationsIssuedATT-AISC-2025-0427

aisecurity.llc

AI SECURITY · PRIVACY · TRUST

SECURITY REVIEW ATTESTATION

Independent Assessment · Evidence-Based · Public-Safe

E

Example Client

example.com

Example Client engaged aisecurity.llc to conduct a security review of the systems, processes, and public trust surfaces described below.

🔍

Map + Defend · AI Product Security Review

Architecture, threat modeling, controls, and risk analysis.

🗄️

Map + Defend · RAG Authorization Review

Authorization, data access controls, retrieval boundaries, and prompt/data safety.

🎯

Attack · AI Red Team & Adversarial Testing

Controlled adversarial testing, prompt injection, abuse, and misuse scenarios.

🛡️

Prove · Trust Surface and Evidence Review

Public-facing policies, evidence, and trust artifact evaluation.

Systems / Features in ScopeCustomer-facing AI application, retrieval-augmented generation system, supporting APIs, and related data stores as described by the client.
Review TypeWhite-box review, architecture analysis, configuration review, and controlled testing.
Engagement IDAISC-2025-0427
Engagement PeriodApril 15, 2025 – April 30, 2025
Report DeliveredMay 2, 2025
92/ 100

Strong

Within the reviewed scope and evidence available during the engagement period, the reviewed systems demonstrated a strong AI security posture. Identified findings were documented, prioritized, and paired with a remediation path. This result does not apply to unreviewed systems, later changes, or future threats.

3High findings
7Medium findings
12Low findings
4Informational
✓

AI Security

AI surfaces, prompts, model boundaries, provider paths, and system behavior.

✓

Application Security

Authentication, authorization, and input validation.

✓

Data Security

Data classification, encryption, and handling.

✓

Identity & Access

IAM controls, privilege, and access boundaries.

✓

RAG & Data Access

Retrieval pipeline, corpus trust, authorization boundaries, tenant/data access, and output handling.

✓

Infrastructure Security

Network controls, configuration, and exposure.

◑

Privacy & Legal

Data handling, consent, and regulatory alignment.

✓

Monitoring & Detection

Telemetry, logging, alerting, abuse detection, and evidence capture for AI-specific failure modes.

◑

Incident Response

Escalation paths, playbooks, and AI-specific scenarios.

✓

AI Governance

Policies, accountability, and model lifecycle.

✓

Vendor / Third Parties

Supply chain, model providers, and integrations.

✓

Secure Operations

Deployment practices, secrets, and change control.

◑

Business Continuity

Resilience, failover, and degraded-mode safety.

✓

Public Trust Surface

Trust center, disclosures, and buyer-facing claims.