Security Services

AI Security & Governance

Turn AI adoption into a controlled, repeatable process with risk assessment, policy, vendor review, adversarial testing, and regulated-deployment support.

Scoped entry points Written authorization Evidence and follow-up
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Overview

AI adoption creates security exposure that traditional security programs don't address: shadow AI tools processing sensitive data without vendor review, LLM-powered features with unvalidated trust boundaries, model vendor dependencies with opaque data practices, and regulatory obligations that apply specifically to AI-assisted decisions.

This path provides the security and governance architecture for responsible AI adoption: risk assessment that supports leadership decisions, operating policies that are specific enough to follow, vendor and model review that goes beyond self-reported questionnaires, adversarial testing that finds the misuse paths normal QA misses, and compliance architecture for regulated AI deployment.

These services bridge AI integration and cybersecurity — treating AI systems with the same rigor applied to any other piece of critical infrastructure that handles sensitive data or makes consequential decisions.

How you start
Board-Ready AI Risk Assessment when leadership needs a structured view of AI risk before governance decisions
AI Vendor & Model Review when specific procurement decisions need independent risk evaluation
AI Red Team & Misuse Scenario Review when AI features need adversarial validation before production deployment
Scope and evidence
AI governance work follows a documented framework — not ad hoc policy invention
Adversarial testing uses security-grade methodology adapted for AI-specific attack surfaces
Regulated-use deployment support maps to specific regulatory requirements, not generic AI ethics language

Scope Pattern

Pressure patterns that usually lead here.

Organizations deploying AI in business-critical or regulated workflows who need governance that's enforceable, not just aspirational.

Boundaries

No empty promises or checkbox exercises.

Engagements stay grounded in written scope, lawful work, and the level of evidence or follow-through your environment actually needs.

Discovery

Clarify the first move and what comes next.

Discovery should clarify your environment, urgency, who needs to see results, and whether the first move is an assessment, a deeper project, or ongoing support.

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Included Services

Services that can support this path.

These services can be scoped independently or sequenced together once the right starting point, environment, and urgency are clear.

Board-Ready AI Risk Assessment

Give leadership a structured view of AI-related risk across data handling, vendor exposure, output reliability, and regulatory implications — in language that supports budget and governance decisions.

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AI Policy & Operating Rules

Define clear rules for how your organization uses AI — covering approved tools, data boundaries, human review requirements, and escalation paths — before ad hoc adoption creates ungoverned risk.

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AI Red Team & Misuse Scenario Review

Test selected AI systems against agreed adversarial inputs, misuse scenarios, and edge cases before or after deployment.

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AI Vendor & Model Review

Evaluate the security, privacy, and operational risk of AI vendors, models, and third-party AI services before they become embedded in your business workflows.

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Regulated-Use AI Rollout Support

Deploy AI into regulated workflows — healthcare, financial services, legal, or government — with the compliance controls, evidence requirements, and audit trail design the environment demands.

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Governance Rollout

Turn governance decisions, policies, and framework outputs into operating practice — with rollout support, owner alignment, and evidence that adoption actually happened.

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