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What is AI Runtime Security?

It is the set of live controls that watch what your AI does while it is working, the actions it takes, the tools it calls, the data and memory it reaches for, and step in the moment it crosses a line it shouldn't.

Think of a new AI feature as a new employee. Governance is its job description and the company rulebook: what it is hired to do, what it must never do, and who is accountable when something goes wrong. Most organisations already have this part.

But a rulebook on its own never stopped anyone. Runtime security is the supervisor standing over their shoulder: checking the work in real time, catching mistakes before a customer ever sees them, and pulling the plug the moment something goes badly wrong.

Governance decides what AI should do. Runtime security verifies what it actually does.

Why does it matter now?

For years, AI mostly answered questions. Now it acts: it books, buys, sends, edits, and reaches into other systems on your behalf. A wrong answer is awkward. A wrong action has consequences in the real world.

And many of the most dangerous failures begin the same way: untrusted content, a tool, a stored memory, or borrowed authority quietly steering the AI while it runs. The classic version is a hidden instruction dressed up as ordinary content: a web page, a document, or an email the AI reads can tell it to do something it was never meant to do. Catch those moments and a long list of named threats loses much of its teeth.

So the surface worth watching is wider than the words going in and out. It includes who the AI is acting as and how far that authority reaches, what it remembers, the tools and models it trusts, and what it costs to run. The framework treats each of these as a place to put controls, not an afterthought. See the wider boundary →

How AIRS handles it

AIRS answers it with four independent layers. Each does one job, and each keeps working even if the others fail.

Layers over an AI system: guardrails filter in real time, reviewing controls (scanners, a semantic firewall, and a model-as-judge) check the output, and human oversight handles escalated decisions
Three layers sit over your AI system. The reviewing layer is itself three independent checks. Circuit breakers stand behind them as the failsafe.
01
Guardrails

Block the obvious failures instantly.

02
Reviewing controls

Check the output before anyone sees it.

03
Human oversight

Put a person on the high-stakes calls.

04
Circuit breakers

Stop everything and fail safe when needed.