Definition
AI Security addresses the unique threats and vulnerabilities associated with artificial intelligence and machine learning systems, including adversarial attacks, model poisoning, data privacy, and the security of AI-driven decision-making.
People Also Ask
Common questions about AI Security answered by our security experts.
AI Security in Practice
Securing an AI system means defending things a conventional application security programme was never built to consider. The code may be sound, the infrastructure hardened and the access controls correct, and the system can still be manipulated through the one channel it exists to accept: its input.
That is the structural difference. Traditional software separates instructions from data — a parameterised query is exactly that separation. A model receives one stream and has no reliable mechanism for deciding which parts of it carry authority.
The risk classes worth separating
Lumping these together produces a policy nobody can act on. They have different causes and different defences.
| Class | When it happens | What it looks like |
|---|---|---|
| Prompt injection | Inference | Hostile instructions ride in on content the model reads |
| Adversarial evasion | Inference | Crafted input makes the model classify wrongly |
| Model poisoning | Training | Compromised data or weights introduce a backdoor |
| Model theft | Either | Weights or behaviour extracted through repeated queries |
| Data leakage | Either | Training data or another user's context surfaces in output |
| Supply chain | Build | A pulled model, dataset or library is not what it claims |
The first three have their own entries because the defences genuinely differ. Treating them as one problem called “AI risk” is why so many AI policies say nothing actionable.
Where the real exposure usually sits
For most organisations the pressing question is not whether a model can be tricked in a laboratory. It is what the model is permitted to do when it is.
That is why the highest-value control is rarely a model-level defence. It is retrieval scoped to the individual user's permissions rather than a service account's, and human confirmation before anything consequential.
Governance that is proportionate
Not every AI feature warrants the same scrutiny. Triage on consequence: does the system affect a person's access to care, employment, credit, housing or legal position? Those need documented assessment, meaningful human oversight and a route to challenge a decision. A summarisation feature over public content does not.
Applying the heavy process everywhere guarantees it is applied nowhere, because teams route around it.
The question that orders everything else
Before any framework or tooling decision, answer this: for each AI system, what happens if it is wrong, and who finds out? A model whose error produces a mildly unhelpful summary needs almost no governance. A model whose error denies someone care, credit or a job needs documented oversight and an appeal route. Most organisations have both and govern them identically, which is why the policy feels simultaneously burdensome and inadequate.
Where to start
List what your AI features can do without a human confirming. If anything on that list is consequential and also processes content from outside your organisation, put a confirmation step there. It is usually a small change and it removes most of the realistic damage.
Further reading: AI governance and ISO 42001, the questions that reveal how an AI vendor uses your data, and finding the tools staff already adopted. GuardsArm runs security risk assessments covering AI deployments.
Related Terms
Threat Intelligence
Threat intelligence is evidence-based knowledge about existing or emerging threats to an organization's security. It includes context, indicators of compromise (IOCs), and actionable insights that help organizations understand, prevent, and respond to cyber threats.
Security Operations Center (SOC)
A Security Operations Center (SOC) is a centralized facility staffed with security analysts who continuously monitor, detect, analyze, and respond to cybersecurity incidents using a combination of technology solutions and processes.
Advanced Persistent Threat
An Advanced Persistent Threat (APT) is a prolonged, targeted cyberattack in which an intruder gains access to a network and remains undetected for an extended period. APTs typically target high-value organizations to steal data rather than cause immediate damage.
Related Reading
AI Governance and ISO 42001: Where to Start Before You Certify
An AI management system is ISO 27001 in shape and different in substance. What it actually requires, and the inventory that has to come first.
AI Vendor Risk: The Questions That Reveal How Your Data Is Used
Standard vendor questionnaires miss training use, retention of inputs and the model supply chain behind the product you are buying.
Shadow AI: Finding the Tools Your Staff Already Pasted Data Into
Blocking it does not work and the data has usually already gone. How to find out what is in use and provide a route people will actually take.
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