ConsultingWorkbench-backed AI security engagements — map, attack, defend, and prove your AI systems.
Scope a Review
← 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 Validation

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, 2025April 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.