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AtelaMind
Capability

AI Security Evaluation & Assurance

Independent assessment of production AI systems — testing threat exposure, control effectiveness, and evaluation coverage to produce decision-ready findings and assurance evidence.

  • Adversarial simulation mapped to real misuse scenarios and AI-specific attack paths
  • Severity-rated findings with prioritized remediation and fix verification
  • Audit-ready evidence packs documenting what was assessed, observed, and concluded
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AI Security Evaluation & Assurance
When to bring this in

This is typically needed when:

Controls appear functional on paper but have not been empirically validated against real misuse and failure modes.

A production launch or scaling decision needs defensible assurance evidence — not just a demo or internal review.

Post-incident investigation has revealed undocumented assumptions, missing logging, or gaps in control coverage.

Security, risk, or compliance teams need an independent view of what holds, what fails, and what must change.

The system has evolved since the last review, and there is no repeatable cadence for validating that controls still work.

What the engagement covers

Scope

A principal-led assessment that tests production AI surfaces against real threat scenarios, produces severity-rated findings, and delivers the evidence needed for release, audit, and remediation decisions.

Adversarial simulation against injection, leakage, retrieval abuse, unsafe tool actions, privilege escalation, and grounding failures
Control effectiveness testing: do boundaries, permissions, guardrails, and evaluation gates actually hold under pressure?
Findings mapped to OWASP LLM Top 10, OWASP Top 10 for Agentic AI, and MITRE ATLAS attack paths
Assessment methodology structured around CSA MAESTRO, with evidence traceability aligned to NIST AI RMF
Traceability review: logging, telemetry, evaluation artifacts, and decision evidence — what exists, what is missing, what is insufficient
Remediation priorities sequenced by severity, exploitability, and delivery impact
Fix verification criteria and re-check expectations to confirm remediation closure
Assurance cadence design: pre-release checks, evidence requirements, and ongoing monitoring signals
Typical outputs

What the engagement produces

What changes afterwards

After this engagement

Security and risk teams have a decision-ready view of what is acceptable, what is fragile, and what must change before scaling.

Findings are severity-rated with clear ownership — not a generic list of recommendations without prioritization.

Release and scaling decisions become more defensible because they are backed by empirical evidence, not assumptions.

Remediation follows a sequenced path that protects delivery momentum instead of blocking everything at once.

Assurance becomes repeatable — controls and evidence stay current as the system evolves, not just at launch.

What this is not

✕A penetration test or network security audit
✕A compliance checkbox exercise
✕A hands-on engineering remediation team
✕A vendor security assessment in disguise
✕A one-time review with no follow-through path
Common questions

FAQs