Independent AI Security Review before the Auditor Asks
from $60K. 6 to 10 weeks.
4 to 8 weeks, scoped to severity.
from $25K. 3 to 4 weeks.
Where Do You Start?
Two situations we see most often.
You Have an AI System Live or About to Launch
Your AI system is in production or pre-launch. Internal audit has flagged it. The board has asked for a security posture statement. The regulator has signaled scrutiny. You need an independent review by practitioners who build AI systems for a living, not generalists extending an application security practice.
AI Security Review (6 to 10 weeks, from $60K).
Threat model specific to your system. Red-team execution against OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, and curated corpora. Findings report with severity scoring and reproduction steps. Framework alignment scorecard: EU AI Act, NIST AI RMF, ISO 42001, plus sector-specific (HIPAA, DORA, SEBI). Remediation roadmap with effort estimates. Board-ready executive summary.
You Have a Deadline and the System Isn't Ready
A regulatory deadline is approaching. EU AI Act enforcement window. Internal audit closure date. Pre-launch security sign-off. You don't have time for a full review followed by a remediation roadmap. You need both compressed into a single fixed-fee engagement.
Pre-Deadline Remediation (4 to 8 weeks, fixed-fee scoped to severity and deadline).
Targeted threat assessment focused on the specific compliance or audit gap. Critical-severity findings remediated in-engagement. Documentation produced to satisfy the audit or regulatory requirement. SOW commitment to deadline.
What an AI Security Review Covers
A 6 to 10 week engagement delivering an independent assessment of your AI system against the threat models, frameworks, and adversarial techniques that matter for production AI in regulated industries. Below is what the engagement produces.
The Threat Model
- Architecture & Boundaries: System architecture review and trust boundary mapping.
- PII & Sensitive Data: Data flow analysis with PII and sensitive data tagging.
- Access & Trust: User population analysis (internal, customer, third-party).
- Attack Surfaces: Threat surface enumeration with attack vectors per surface.
The Red Team
- OWASP LLM Top 10: Adversarial testing against the industry standard for LLM security.
- NIST AI RMF: Alignment with risk management guidelines.
- MITRE ATLAS: Mapping to threat tactics and techniques.
- Adversarial Corpora: Prompt-injection, jailbreak, and data-exfiltration testing.
- Supply Chain: Model-extraction and supply-chain attack scenarios.
- Vulnerability Scoring: Findings delivered with CVSS-aligned severity scoring, reproduction steps, and prioritized remediation.
The Framework Alignment
- Regulatory regimes: EU AI Act (live now).
- Risk standards: NIST AI Risk Management Framework & ISO 42001.
- Sector compliance: HIPAA, DORA, SEBI, and sector-specific BFSI requirements.
- Internal controls: Your enterprise AI governance policies (if applicable).
- Final deliverables: Delivered with gap identification, remediation effort estimates, and a board-ready summary.
Engagement investment: from $60K. Duration: 6 to 10 weeks. Deliverables: threat model, findings report, framework alignment scorecard, remediation roadmap, executive summary.
Demonstrating AI governance posture to your board, audit, and regulators?
Who's on the Team
The practitioners who execute the review build AI systems in production. This matters for AI security work in a way it doesn't for generic application security. The threats are different and the defensibility of findings depends on understanding what production AI systems actually look like.
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Lead Practitioner, AI Security
12+ years in security, with the last 4+ in AI systems specifically. Owns threat model, findings quality, and engagement delivery.
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Senior Engineer, Adversarial Testing
Deep on prompt injection, jailbreak technique, model extraction, and adversarial corpora. Executes the red team.
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Senior Engineer, Framework & Compliance
Owns framework alignment mapping, regulatory translation, and the executive summary. Ensures findings translate into language the board, audit, and regulator can act on.
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Delivery Lead
Engagement cadence, weekly status, milestone acceptance. Single contact for engagement status.
How We Approach the Work
AI security is not application security with AI bolted on. The threat model is different. Prompts are a new attack surface. Training data is a supply chain. Outputs are a disclosure vector. Most penetrating firms are extending their existing practice into AI. We built ours around AI systems specifically.
Findings are reproducible, not theoretical. Every finding in our report is reproducible with the steps we provide. No finding is "this might be vulnerable." Either we demonstrated the vulnerability or we didn't include it.
Framework alignment is delivered as scorecards, not narratives. Compliance teams need scorecards they can take to audit. We deliver alignment to EU AI Act, NIST AI RMF, ISO 42001, and sector-specific frameworks as scored mappings with gap identification. Narrative documents are an addition, not the primary deliverable.
The executive summary is written for the board. Findings are translated into business risk language, with remediation effort estimates and prioritization recommendations. The report your CISO can take to the board on Tuesday without rewriting it.
Technology and Platform Posture
We review AI systems regardless of model provider, retrieval architecture, or deployment platform. Below is what we cover.
Model Providers Reviewed
Anthropic 
OpenAI 
Azure OpenAI 
Amazon Bedrock 
Google Vertex AI Open-weight models via Hugging Face. Multi-model and provider-agnostic architectures included.
System Architectures Reviewed
RAG Systems 
Agentic Systems 
Fine-tuned Models 
Embedded AI Embedded AI in existing applications. Standalone AI products.
Deployment Environments
AWS 
Azure 
Google Cloud On-premise. Air-gapped and sovereign deployments. Third-party SaaS AI products in your stack.
Independent practitioner review. We do not sell the AI system we audit.
Bring the system. We return with a threat model, review scope, and SOW.
What to Expect from a Review
AI Security Reviews typically produce findings in the following ranges. Severity distribution varies by system maturity and scope. Specific severity targets are set during engagement scoping.
3 to 8 typical for production-grade systems.
5 to 15 typical.
under 2 weeks.
Your specific findings depend on the system under review. We do not predict outcomes. We report them. The above ranges reflect what we typically find, not what we commit to find.
From the Security Practice
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Scope a security review
Scope a Security Review
Tell us about the system. Production or pre-launch, model posture, sector, deadline if any. Thirty minutes is enough to know what shape the engagement should take. NDAs executed before scoping discussion. If we're not the right firm for what you need, we'll point you to who is.