Artificial intelligence is rapidly becoming embedded in enterprise decision making – for better or worse.
From fraud detection and customer service to cyber security analytics and risk scoring, AI models now influence outcomes across the face of modern business.
But while organisations are investing heavily in deploying AI, fewer are asking the uncomfortable question: how secure is the intelligence these systems are built on?
For CISOs, this represents a new class of risk. Traditional security models focus on protecting systems, networks, and data. AI introduces something new: systems that learn, adapt, and evolve based on the data they consume.
If that data is compromised, biased, or manipulated, the AI’s outputs may be flawed – quietly, persistently, and dangerously.
The most immediate concern is the integrity of training data. Many AI models rely on huge datasets aggregated from internal systems, third-party providers, or open-source repositories. If adversaries can poison that data – by injecting false signals, skewed samples, or malicious patterns – they can influence outcomes without ever breaching a perimeter.
The result may not look like a cyber attack, but its impact can be just as severe.
Data drift compounds the problem
Business environments change constantly, but models trained on yesterday’s assumptions can produce dangerously outdated results. Without continuous validation, AI systems may miss emerging threats, misclassify behaviour, or reinforce flawed decisions and introduce bias. For security teams, this creates a false sense of confidence: the model is running, alerts are flowing, but – in the background – accuracy is quietly degrading.
There is also a growing reliance on synthetic data to accelerate AI development. While synthetic data can reduce privacy risks and fill gaps, it introduces new governance challenges. Poorly generated synthetic datasets can amplify bias or embed unrealistic patterns, undermining trust in a model’s outputs. CISOs must understand not just where data comes from, but how it is created.
Accountability is another unresolved issue. When an AI-driven system makes a bad decision – blocking a legitimate transaction, missing an intrusion, or triggering regulatory scrutiny – who is responsible? The model? The vendor? The data pipeline? CISOs need clear answers before incidents occur, not after.
Controls must extend across the entire AI life cycle: data sourcing, model training, deployment, monitoring, and retraining. This includes access controls on training datasets, audit trails for model changes, validation of outputs, and continuous testing against adversarial scenarios.
Just as importantly, AI governance cannot rest solely upon security teams. Legal, compliance, data science, and business leaders all have a stake. CISOs should push for clear ownership models and shared accountability frameworks that define who approves models, who monitors risk, and who intervenes when something goes wrong.
The message to executives is clear: AI is not a black box that can be trusted by default. It is an evolving system shaped by the data upon which it is built. If that data is compromised, so is the intelligence it produces.
For CISOs, protecting AI isn’t about slowing innovation – it’s about ensuring that automation doesn’t quietly become a liability.
David Hollingworth
David Hollingworth has been writing about technology for over 20 years, and has worked for a range of print and online titles in his career. He is enjoying getting to grips with cyber security, especially when it lets him talk about Lego.