AI Cybersecurity Risks and How to Manage Them

Artificial intelligence is doing real work inside businesses now: handling customer interactions, optimising supply chains, supporting decisions that people used to make on their own. Every one of those systems is also a new attack surface, and the risks it carries are not the ones traditional application security was built to handle.

For organisations running AI at scale, that difference is where the work is.

The AI Threat Landscape

AI systems are exposed at every stage of their lifecycle: development, training, deployment and operation. The categories below are the ones that matter most.

Data Poisoning

Attackers manipulate training data to change how a model behaves. This is most dangerous for models that retrain continuously on user feedback or operational data, where there is a constant supply of attacker-influenced input. Poisoned data can introduce bias, plant a backdoor, or simply degrade performance in ways that are hard to detect.

Model Theft and Extraction

A proprietary model is intellectual property, and it can be copied through its own interface. With enough API queries, an attacker can extract sufficient information to replicate the model's behaviour. The copy exposes what the model can do and, just as usefully, where it is weak.

Adversarial Attacks

Carefully crafted inputs make models produce incorrect outputs. In computer vision, pixel changes that are invisible to a person are enough to force a misclassification. In NLP, a subtly modified prompt can walk past the safety guardrails.

Privacy Violations

Models memorise and reproduce their training data, personal information included. Where that data came from customers without adequate anonymisation, the result is a GDPR liability as much as a trust problem.

Supply Chain Compromise

Most organisations build on pre-trained models, third-party libraries and external datasets rather than training from scratch. Every one of those is a dependency you did not write, and a compromised model or library propagates to every system that uses it.

A Framework for AI Risk Management

None of this needs a separate risk function. It needs the existing one extended to cover things it was not designed to see.

1. Inventory and Classify

Maintain a comprehensive inventory of all AI systems, including:

• What models are in use (including third-party and open-source)
• What data they access and process
• Where they're deployed and who has access
• What decisions they influence

Classify each system based on criticality and data sensitivity.

2. Threat Model

Apply threat modeling to AI systems just as you would to any application. Consider:

• Who would want to attack this system and why?
• What inputs does the system accept and how can they be manipulated?
• What's the impact of incorrect or manipulated outputs?
• What data could be extracted through the system?

3. Implement Controls

The threat model tells you which controls are worth building.

Start with what goes in. Filter and validate every input to an AI system, which for an LLM means prompt filtering and injection detection. Watch what comes out as well, for anomalies, bias and policy violations, and keep a human in the loop on decisions that carry real consequences.

Least privilege applies to models, training data and inference APIs exactly as it does everywhere else, and AI infrastructure belongs on its own segment rather than sitting on the general corporate network.

Then track model performance over time. Drift, degradation and signs of tampering all surface as changes in behaviour, and none of them are visible without a baseline to compare against.

4. Test and Validate

Regular testing should include:

• Red team exercises focused on AI-specific attack vectors
• Adversarial testing against prompt injection and data extraction
• Privacy audits to verify data handling compliance
• Supply chain reviews of model and library dependencies

5. Prepare for Incidents

Extend your incident response plan to cover AI-specific scenarios:

• Model compromise or manipulation
• Training data breach
• Adversarial exploitation in production
• Regulatory inquiry related to AI decisions

Governance and Accountability

Technical controls do not survive contact with an organisation unless someone owns them. AI security needs a sponsor at board level, a named owner inside the existing risk management framework rather than a parallel one, and regular reporting on posture to leadership. It also needs a working relationship between the data science, engineering, legal and security teams, each of whom holds one piece of the problem and none of whom can see all of it.

Moving Forward

AI cybersecurity risk management is still an immature discipline, and the ground keeps moving. NIST's AI Risk Management Framework and the EU AI Act's risk-based classification system are both recent, and neither is the last word.

That is not a reason to wait. The organisations that start managing these risks now, imperfectly, will be in a far better position than those holding out for a settled standard. There is not going to be a moment when the guidance is finished and the work can begin.