For Security & CISO teams
AI Security and Governance for Security Leaders
You’ve secured the network, the endpoints and the cloud. Now the business has deployed AI everywhere — and the attack surface just moved.
AI is a class of risk that does not fit existing models. It is not a device, a server or an application. It is a system that can be prompted, poisoned, exfiltrated and — increasingly — trusted with actions.
Runtime enforcement
What changed
Three shifts happened at once.
None of them are covered by the controls you already have, and the first is the largest because it is invisible.
Adoption outran approval
Employees put regulated data into systems security never reviewed. The largest exposure is the one nobody logged.
Shadow AIModels became targets
Worth stealing, worth poisoning. Prompt injection, extraction and unsafe outputs are attack classes appsec does not cover.
New attack surfaceAgents began acting
Autonomous systems take actions across internal tools, which turns a bad output into a bad event.
Actions, not answersPrioritise
Score risk before you secure it.
Not every model deserves the same level of protection. A public chatbot touching marketing content is low risk; an agent with access to customer records is high risk. Score on four axes, then spend accordingly.
Risk scoring is what tells you where scarce security budget belongs — and, just as usefully, where it does not.
Protect
Four assets worth defending.
AI risk concentrates in a small number of places. These are they.
Models
Against theft, extraction and poisoning.
Data
Against exposure through prompts, outputs and training.
Credentials
Against agents and tools that over-reach.
Infrastructure
The runtime your AI depends on.
Security without governance locks down the models you know about, while the business quietly works around you. Governance without security produces policies nothing enforces.
Enforce
Enforce, don’t just document.
The gap between policy and reality is where breaches happen. Runtime enforcement — controls that apply dynamically based on policy, risk and context — closes that gap. When something does go wrong, the evidence trail is what lets you investigate and prove what happened.
Autonomous systems need the same controls applied per action rather than per request — see agentic AI governance. Systems nobody registered can’t be protected at all — see shadow AI.
- Discovery and inventory of every AI system, including unapproved tools and agents.
- Access and privilege controls scoping what each system may reach.
- Runtime enforcement applying policy as systems act, not at setup.
- Threat protection against prompt attacks, data exposure and unsafe outputs.
- Traceability and analytics to investigate incidents and detect drift.
Questions
Security FAQ
How is AI security different from application security?
An application behaves the same way each time it runs; an AI system can be prompted into behaviour nobody specified. That adds attack classes traditional appsec does not cover — prompt injection, data poisoning, model extraction, unsafe outputs — and, with agents, the system can take actions rather than only returning data.
Where should a security team start with AI risk?
Inventory, then risk scoring. You cannot protect systems you have not enumerated, and scoring tells you where scarce budget belongs. A public chatbot touching marketing content and an agent with access to customer records need very different treatment.
What is runtime enforcement, and why does it matter?
Controls applied dynamically as an AI system acts, based on policy, risk and context — rather than configuration checked at deployment. It matters because a system configured safely in January can behave differently in March as its inputs, model version and integrations change.
Does this replace our existing security stack?
No. It governs a layer the existing stack does not see: which models, tools, data and actions each AI system may reach, and what it actually did. It draws telemetry from the AI platforms and frameworks already in use rather than replacing them.
How do we score AI risk?
By business impact, data sensitivity, autonomy and exposure. Those four together separate the systems that warrant real investment from the majority that do not.
What evidence will we have after an incident?
A connected trail of what the system reached, what it did and in what order — across models, tools and downstream systems. That is what turns an investigation into a reconstruction rather than a guess.
Get Started
Bring AI risk into your security programme.
Enforceable controls, risk scoring and audit-ready evidence across the whole AI estate.