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 going into customer-facing products, internal tools and core business processes. Security governance, in most organisations, has not moved at the same pace.

That gap between AI capability and AI security is where the liability sits, and it lands on the CISO whether or not the CISO was consulted about the deployment.

The Security Challenge Is Different

GenAI breaks several assumptions that traditional application security depends on:

• Prompt injection lets an attacker steer model behaviour through crafted input, routing around restrictions you thought were in place.
• Data leakage follows from what a model was trained on or can reach at run time, and it surfaces in responses nobody reviewed.
• The model supply chain now includes third-party models, fine-tuning datasets and embedding pipelines, few of which get the review a third-party code library would.
• Output reliability is uncertain by design. A model will produce answers that are plausible, confident and wrong. The industry word for this is hallucination, which rather undersells the liability.
• Shadow AI spreads whenever employees put corporate data into consumer tools, outside anything IT can see.

The controls, the governance frameworks and the skills to handle any of this are mostly not yet in place.

Six Foundational Security Domains

The work coming out of OWASP and NIST, together with what the EU AI Act requires, points at six domains worth building a programme around.

1. AI Governance & Strategy

Define acceptable use, risk tolerance and accountability. This needs an executive owner, and it needs to sit inside the risk management process you already run rather than alongside it.

2. Data Security & Privacy

Control 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 whole model lifecycle: selection, evaluation, deployment and monitoring. That means assessing models for vulnerabilities, securing the API endpoints that expose them, and watching for adversarial inputs.

4. Application Security

Use the OWASP Top 10 for LLM Applications as your baseline for AI-powered applications. Input validation, output filtering and rate limiting are the minimum.

5. Infrastructure & Operations

The infrastructure supporting AI workloads should meet the same security standard as any other production system: network segmentation, logging, access control, incident response. There is nothing novel in that list, which is precisely why it gets skipped.

6. Compliance & Regulatory Alignment

Map AI activities against the regulations that apply to you: the EU AI Act, GDPR, sector-specific rules, and the AI governance standards still taking shape. Do the mapping while systems are being designed rather than after they ship.

Practical Steps for CISOs

Start with an assessment. You cannot manage exposure you have not inventoried, and the inventory has to cover the unsanctioned projects as well as the approved ones. The unsanctioned list is usually the more revealing of the two.

Write an acceptable use policy that someone can act on. Say what is permitted, what requires approval and what is prohibited, in enough detail that an engineer reading it can tell which category their planned integration falls into.

Extend the processes you already have instead of standing up a parallel programme. Vulnerability management, incident response and risk assessment all need to cover AI systems, and all three are easier to extend than to duplicate.

Invest in the skills. Your security team needs to understand how LLMs work, how they fail and how they are attacked. That is a new competency, and it does not arrive from a single training session.

Engage with the OWASP community. The OWASP Top 10 for LLM Applications is the most practical resource available for AI application security: community-driven, updated regularly, and grounded in attack patterns people are actually seeing.

The Bottom Line

GenAI is not a passing trend, and it will be embedded in enterprise operations for years. The foundations are unglamorous: governance, data controls, model security, compliance alignment. Put them in now and you get to shape how AI is deployed across the business. Wait, and you will be doing the same work under incident conditions, with less time and fewer options.