The Journey
Assess
Risk Discovery
Identify AI security risks, shadow AI usage, and maturity gaps.
D3 Cyber Services
Architect
Governance Design
Build AI governance frameworks, policies, and security architecture.
D3 Cyber Services
Manage
Lifecycle Control
Active AI safety monitoring and review, incident response, and cost optimization.
D3 Cyber Services
The AI Security Lifecycle
A structured approach to deploying AI safely - from risk discovery to continuous governance.
Phase 1: Assess
Weeks 1-6
Discovery & Testing
- AI/LLM Penetration Testing
- Shadow AI Discovery (Unsanctioned Tools)
- EU AI Act Risk Classification
Maturity Baseline
- AI Maturity Assessment (AIMA)
- Gap Analysis Against Industry Benchmarks
- Prioritized Improvement Roadmap
Phase 2: Architect
Months 2-3
Policy & Framework
- AI Acceptable Use Policy (AUP)
- Multi-Framework Governance Framework
- Data Classification for AI Consumption
Security Architecture
- AISVS Security Requirements (L1-L3)
- Model Sovereignty & Selection Governance
- Deterministic Policy Kernels for Agents
Phase 3: Manage
Ongoing
Ongoing Monitoring
- Quarterly Red-Team Exercises
- Model Drift & Output Quality Tracking
- Bias Detection and Alerting
Lifecycle & Compliance
- AI Incident Response Playbooks
- Continuous Safety Evaluation (AISVS, AIMA)
- ISO 42001 AIMS Certification Support
- Agentic Runtime Governance & Policy Audit
Why do I need Secure AI?
AI is an accelerator. Without brakes, it's just a crash waiting to happen.
The rapid adoption of GenAI has outpaced traditional security controls. According to recent industry research, 73% of organizations deploying LLMs have at least one critical vulnerability. Employees are pasting sensitive data into public chatbots, autonomous agents are making business decisions without oversight, and nobody knows who owns the model after the POC team leaves.
Governance isn't about blocking innovation. It's about building the guardrails that allow you to deploy AI fast without creating existential business risks.
The AI Governance Reality
- 1
Visibility: You can't secure Shadow AI you don't know exists.
- 2
Compliance: EU AI Act fines reach 7% of global turnover for High-risk systems.
- 3
Maintainability: POC-to-production AI systems break within 6 months.
Company secrets pasted into public ChatGPT
Enterprise instances with zero-retention policies
No understanding of AI-specific vulnerability patterns
Regular red-team exercises against agentic workflows
Opus-class models for email triage (wasteful spending)
Model right-sizing and inference cost forecasting
No playbooks for prompt injection or data poisoning
AI-specific incident classification and EU AI Act reporting
We don't block innovation. We partner with you to build safely.
The D3 Perspective
Field observations from securing AI systems in production.
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.
The Sovereignty
Imperative
The EU AI Act and GDPR have fundamentally changed the landscape. Using non-compliant US-based models for European citizen data is no longer a grey area - it's a liability. Directors can be held personally accountable for algorithmic failures. According to a 2025 study, 68% of organizations using AI have unsanctioned tool usage they're unaware of.
Why D3 for AI Security?
We test AI systems, not just talk about them. We build governance that engineers actually follow.
- We Test AI Systems: Full AI/LLM security coverage, not just automated scans.
- Framework Contributors: We contribute to global AI security projects. Field-tested methodology.
- Vendor Agnostic: Anthropic, OpenAI, open-weight, or sovereign - we recommend what fits your use case.
Why D3 Cyber?
We Test AI, Not Just Talk
We test production AI systems against global security standards, not just written policies. We understand agentic attack chains, MCP server abuse vectors, and prompt injection vulnerabilities in complex tool-calling environments.
Governance That Engineers Follow
We build governance integrated into CI/CD pipelines, using deterministic policy kernels rather than just written rules. We implement technical guardrails that enforce security without slowing down development teams.
Field Perspective
We understand what breaks in production - from POCs that fail within 6 months to silent agentic failures. We prevent these patterns by implementing policy-as-code kernels that intercept and validate autonomous actions.
Regulatory Intelligence
We align requirements across global standards: EU AI Act, ISO 42001, NIST AI RMF, and the OWASP AI security ecosystem. Not just theory - we provide field-tested implementations for high-risk AI systems.
Vendor Agnostic
We are not tied to specific model providers. We recommend the right architecture for your jurisdiction and use case - whether it's Anthropic, OpenAI, open-weight, or sovereign European models.
Sustainability Focus
Oversized models for simple tasks are environmentally and financially indefensible. We optimize model selection for specific business impact and carbon footprints, ensuring efficient, sustainable AI operations.
Ready to Secure Your AI?
Let's build AI governance that empowers your team instead of slowing them down.