AI Security Risk Assessment Services

AI features, agents, and internal tools often go live faster than the security program covering them. Exposure goes undocumented, ownership is unclear, and each new integration widens the surface.

Professional AI risk assessment establishes the baseline: which AI systems run, who owns each one, and what risk each one carries.

SOC 2, PCI DSS, and HIPAA reviews can require documented AI-specific risks before the deadline. Insurers and enterprise buyers in regulated industries treat AI as its own risk category, and expect a documented assessment from a certified external team as evidence.

We map your AI systems to the regulations that apply to you and record what auditors expect to see, including ISO/IEC 42001 for teams formalizing AI risk governance.

Buying, integrating, or acquiring an AI product calls for an independent read on its risk before you sign. Vendor documentation describes what the product does, yet often, it says little about how it could be attacked.

We review what the vendor's model touches, how your sensitive data would flow through it, and how the vendor handles that data during training.

From 300,000 scanner findings to a backlog the team could explain line by line

Challenge.

A client preparing for its SOC 2 audit turned on AWS Inspector across AWS accounts and got 300,000+ findings within hours. The inspector reports every vulnerable package it finds, but can't say which ones an attacker can reach, so the team had no way to distinguish real exposure from noise.

Solution.

We traced most findings to a small set of vulnerable base images shared by every account, then fixed the pattern first in the two highest-priority accounts. Priority workloads moved to hardened base images, and Lambda dependencies were patched directly. Findings the environment made hard to exploit were risk-rated, not ignored: each suppression was recorded with a rationale and a named owner.

Impact.

Open critical findings fell from 300,000 to 100 in 7 days, and every remaining item had a documented decision behind it. The client went into its audit with a vulnerability backlog it could explain line by line.

A four-stage pipeline for reviewing AI-generated codez

Challenge.

A product team on Claude Code had built its own skills and commands library. Delivery outpaced security review, and long single-session reviews lost context and gave inconsistent results.

Solution.

We split the review into four bounded stages: source code mapping, threat modeling, a vulnerability scan across priority attack surfaces, and validation in a fresh session. Validation alone removed nine false positives.

Impact.

Roughly 80% fewer false positives than the single-session approach, on about 20% of the standard token budget. We fixed every confirmed vulnerability, all moderate or low severity, including broken access control and hardcoded secrets.