AI Security Governance

Build the framework. Keep it working.

AI Acceptable Use Policy, governance framework, AISVS security requirements, model selection, agentic architecture hardening, ongoing safety evaluation, model lifecycle management, AI incident response, and AI FinOps.

Most AI Policies Are Theater. AI Doesn't Crash, It Fails Silently

Traditional software crashes loudly. AI agents fail quietly: they make plausible-sounding but incorrect decisions, drift away from training distributions, and accrue technical debt until someone notices the business impact. You need AI Governance that goes beyond a documented AI Policy. A strategic framework that steers your organisation toward the secure, responsible, and ethical use of artificial intelligence, protecting your people, data, and reputation at every step.

Is your AI policy enforced or just written?

Who owns your AI model after the POC team leaves?

Are you monitoring model drift and output quality?

Architect - What We Design

Four core deliverables that give you enforceable AI governance, not document theater.

AI Acceptable Use Policy (AUP)

Clear guardrails for institutional and shadow AI usage across the organization.

Tailored AI usage guardrails
Data classification for AI prompts
IP and copyright protection rules
Shadow AI discovery mapping

AI Risk Governance Framework

Governance structure aligning business objectives with EU AI Act and ISO 42001 requirements.

EU AI Act risk tier mapping
ISO 42001 AIMS implementation
Roles and ownership matrix
Regulatory reporting workflows

Security Verification (AISVS)

Customized security verification standards for your unique AI application and model stack.

L1-L3 security verification levels
Adversarial robustness requirements
Training data governance controls
Input validation (Prompt Hardening)

Agentic System Hardening

Security architecture for multi-agent systems and autonomous tool permission boundaries.

Tool permission & MCP boundaries
Least-privilege agent design
Human-in-the-loop enforcement
Cross-agent trust models

Manage - What We Run

Four ongoing governance capabilities to prevent AI lifecycle failures.

Continuous Safety Evaluation

Ongoing monitoring for model drift, adversarial attacks, and emerging safety threats.

Periodic red-team re-runs
Hallucination rate tracking
Bias drift detection & reporting
AISVS re-verification cycles

Model Lifecycle Governance

Governance of model versioning, fine-tuning datasets, and secure decommissioning.

Model versioning and ownership
Fine-tuning data protection
Secure model decommissioning
Maintenance & API oversight

AI Incident Response

Specialized response playbooks for AI security breaches and agentic failures.

AI-specific breach playbooks
System rollback procedures
EU AI Act incident reporting
Silent failure monitoring

AI FinOps & Optimization

Token usage monitoring, model right-sizing, and cost-to-value performance governance.

Token cost attribution
Right-sizing decision matrix
Sustainability metrics
Budget enforcement controls

The D3 Perspective

Field observations from building and managing AI governance in production environments.

Engineering Reality Over Policy Theater

An AI policy buried in SharePoint is worse than no policy. Engineers ignore unenforceable rules, and developers will continue pasting proprietary code into public tools if governance is divorced from their engineering workflow. We build governance integrated into CI/CD pipelines, using technical controls rather than written rules to make security the default.

Agentic Guardrails vs. Prompt Theater

Prompt-based safety is probabilistic and easily bypassed. Real agentic security requires deterministic runtime governance - a "security kernel" that intercepts tool calls and resource access before they execute. We architect systems where agents operate within hard privilege boundaries, ensuring autonomous actions don't turn into unauthorized access.

Human Oversight & Silent Failure Monitoring

AI systems fail quietly - they make plausible-sounding but incorrect decisions. When SOC teams or incident responders stop investigating because the AI summary looks right, you have a cognitive dependency problem. We build monitoring that detects silent agentic anomalies and train teams to verify AI outputs rather than trusting them blindly.

Lifecycle Ownership & Maintenance

AI systems often break within six months because the POC team disbands, leaving nobody to manage model drift or API changes. Governance means defining lifecycle ownership and maintenance workflows before deployment, not after failure. We ensure AI production systems remain maintainable and monitored long after the initial ship date.

Frameworks & Standards

We align with global security standards and regional regulatory requirements.

OWASP AI Exchange

Threat models and controls for all AI system types

OWASP AISVS

Security verification standard with L1-L3 levels

EU AI Act

Risk tier mapping and Article 9 compliance

ISO 42001

Certified AI Management System framework

What You Get

Tangible deliverables across both the Architect and Manage phases.

AI Acceptable Use Policy

Tailored AUP document with data classification tiers, approved tool list, and shadow AI guardrails ready for board sign-off.

Governance Framework Design

Documented AI governance structure aligned to EU AI Act risk tiers and ISO 42001 AIMS, with roles, ownership matrix, and reporting workflows.

AISVS Assessment Report

L1-L3 security verification report against the OWASP AI Security Verification Standard, with gap findings and remediation priorities.

Model Lifecycle Governance

Documented model versioning controls, fine-tuning data protection standards, and secure decommissioning procedures.

AI Incident Response Playbook

AI-specific breach playbooks covering prompt injection, data poisoning, agentic failures, and EU AI Act reporting obligations.

Agentic Runtime Governance

Implementation of deterministic policy kernels to validate autonomous agent actions, tool calls, and resource access against safety boundaries in real-time.

Engagement Model

Retainer-based or project-based governance - your choice.

Retainer Model

Monthly retainer for continuous AI governance oversight. Includes quarterly red-team exercises, monthly drift monitoring, and on-demand incident response.

Best for organizations with 3+ production AI systems.

Project-Based

Fixed-scope engagements for specific governance deliverables. Annual AIMA re-assessments, AISVS verification after major changes, or FinOps optimization projects.

Best for organizations with 1-2 AI systems or specific governance gaps.

Frequently Asked Questions

What is an AI Acceptable Use Policy?

An AI Acceptable Use Policy (AUP) defines how employees can and cannot use AI tools in your organization. It covers data handling, IP protection, approved vs prohibited tools, data classification tiers for AI consumption, and employee responsibilities. Unlike generic IT policies, an AI AUP addresses specific risks like data exfiltration through prompts, copyright infringement via AI-generated content, and compliance with EU AI Act transparency obligations.

What is AISVS?

AISVS (AI Security Verification Standard) is an OWASP project modeled after ASVS, providing a checklist of security requirements for AI-enabled systems. It covers training data governance, bias management, user input validation, model lifecycle management, and adversarial robustness. The standard defines three verification levels: L1 for internal tools, L2 for production systems, and L3 for high-stakes applications. We use AISVS to create objective verification checklists for your AI architecture.

What is a sovereign AI model?

A sovereign AI model is deployed and operated entirely within a specific jurisdiction's legal boundaries, with data residency guarantees. For EU organizations, this means models hosted in EU data centers with EU-based training data and legal entities. Sovereign models are critical for GDPR and EU AI Act compliance when processing European citizen data. The trade-off is typically higher cost and potentially lower performance compared to global public models like GPT-4 or Claude.

What is model drift?

Model drift occurs when an AI model's performance degrades over time due to changes in input data patterns, user behavior, or external conditions. For example, a fraud detection model trained on 2024 transaction patterns may miss 2026 fraud tactics. We monitor drift through regular output quality checks, hallucination rate tracking, and bias detection. When drift is detected, we trigger retraining or model replacement workflows.

What is an AI incident?

An AI incident is any failure, security breach, or unintended consequence caused by an AI system. This includes prompt injection attacks, data poisoning, hallucination-induced business decisions, bias discrimination incidents, and agentic silent failures. The EU AI Act requires High-risk AI systems to report serious incidents to authorities within 15 days. We provide AI-specific incident playbooks and regulatory notification workflows.

How often should AI systems be tested?

High-risk AI systems under the EU AI Act require continuous monitoring. We recommend quarterly red-team exercises against production systems, monthly output quality reviews, and annual AIMA maturity re-assessments. AISVS verification should be re-run whenever significant system changes occur - model updates, new tool integrations, or data source changes.

Why D3 Cyber?

Engineering Reality

We don't write policies that sit in SharePoint. We build governance that engineers actually follow because it's integrated into their workflow.

Operational Reality

We've seen POC-to-production AI systems break within 6 months due to model drift, cost overruns, and ownership gaps. We build lifecycle governance that prevents maintainability crises.

Cognitive Dependency Awareness

When SOC teams stop investigating because the AI summary looks right, you have a cognitive dependency problem. We train teams to verify AI outputs, not trust them blindly.

Multi-Framework Methodology

We normalize requirements across global standards: ISO 42001, EU AI Act, and the OWASP AI security ecosystem.

Vendor Agnostic

We recommend the right model for your use case - Anthropic, OpenAI, open-weight, or sovereign. We're not paid by model providers.

Sustainability Focus

Running Opus-class models for email triage is environmentally indefensible at scale. We help you optimize model selection for business impact, not just technical capability.

Ready to Build Real AI Governance?

Stop writing policies that nobody reads. Build governance that works.

Schedule Governance Review