GenAI Security Foundations: What Every CISO Needs to Know

Generative AI has moved from experimentation to enterprise deployment faster than almost any technology in recent memory. Large Language Models (LLMs) are being embedded into customer-facing products, internal tools, and business processes. Yet for many organisations, security governance has not kept pace with adoption.

This creates a growing gap between AI capability and AI security — one that CISOs need to close before it becomes a liability.

The Security Challenge Is Different

GenAI introduces security challenges that don't map neatly to traditional application security:

• Prompt injection allows attackers to manipulate model behaviour through crafted inputs, bypassing intended restrictions.
• Data leakage can occur when models are trained on or have access to sensitive data, potentially exposing it in responses.
• Model supply chain risks emerge from reliance on third-party models, fine-tuning datasets, and embedding pipelines.
• Output reliability is inherently uncertain — models can generate plausible but incorrect information (hallucinations), creating liability risks.
• Shadow AI proliferates when employees use consumer AI tools with corporate data, outside IT visibility.

These risks require new security controls, new governance frameworks, and new skills within security teams.

Six Foundational Security Domains

Based on emerging frameworks from OWASP, NIST, and the EU AI Act, organisations should address six key domains:

1. AI Governance & Strategy

Establish an AI governance framework that defines acceptable use, risk tolerance, and accountability. This should be owned at the executive level and aligned with existing risk management processes.

2. Data Security & Privacy

Implement controls around what data can be used for training, fine-tuning, and retrieval-augmented generation (RAG). Classify data before it enters any AI pipeline and enforce access controls at the data layer.

3. Model Security

Secure the model lifecycle — from selection and evaluation through deployment and monitoring. This includes vulnerability assessment of models, securing API endpoints, and monitoring for adversarial inputs.

4. Application Security

Apply OWASP Top 10 for LLM Applications as a baseline for securing AI-powered applications. This includes input validation, output filtering, and rate limiting.

5. Infrastructure & Operations

Ensure the infrastructure supporting AI workloads meets the same security standards as any other production system. This includes network segmentation, logging, access control, and incident response.

6. Compliance & Regulatory Alignment

Map AI activities against applicable regulations — the EU AI Act, GDPR, sector-specific rules, and emerging AI governance standards. Build compliance into the AI lifecycle rather than treating it as an afterthought.

Practical Steps for CISOs

Conduct an AI security assessment. Before you can manage risk, you need to understand your exposure. Inventory all AI initiatives — sanctioned and shadow — and assess their security posture.

Establish an AI acceptable use policy. Define what's permitted, what requires approval, and what's prohibited. Make it specific enough to be actionable.

Integrate AI into existing security processes. Don't build a parallel security programme. Extend your existing vulnerability management, incident response, and risk assessment processes to cover AI.

Invest in AI security skills. Your security team needs to understand how LLMs work, how they fail, and how they can be attacked. This is a new competency that requires deliberate investment.

Engage with the OWASP community. The OWASP Top 10 for LLM Applications is the most practical, community-driven resource available for AI application security. It's updated regularly and reflects real-world attack patterns.

The Bottom Line

GenAI is not a passing trend. It will be embedded in enterprise operations for years to come. CISOs who establish security foundations now — governance, data controls, model security, and compliance alignment — will be in a far stronger position than those who wait for a breach to force action.

The window for proactive AI security governance is open. It won't stay open forever.