The AI Shift in InfoSec Priorities

For decades, information security has been grounded in the CIA triad: Confidentiality, Integrity, and Availability. In practice, most security efforts have focused heavily on Confidentiality (keeping data private) and Availability (keeping systems online). That made sense — most of the value in digital systems came from the information they stored and the ability for people or systems to access it.

But AI is shifting that balance.

Integrity Comes First

AI systems — especially those built as autonomous agents — are changing how we think about security. These aren’t just models generating text anymore. They’re tools that can plan, remember, make decisions, and act across systems — sometimes with little human oversight.

That means integrity — the idea that what a system does is trustworthy and hasn’t been tampered with — becomes a bigger deal than ever.

What’s the Real Risk?

When you add memory, multi-step planning, and external tool use to an AI system, a few things happen:

• It can start learning from the environment in ways you didn’t explicitly program
• It can store and reuse information, meaning one bad input can cause long-term weirdness
• It might act on false assumptions without clear traceability

For example:

• Someone embeds a malicious instruction in a shared doc, and the agent reads it and treats it as fact
• The agent starts suggesting biased or unsafe actions based on previous corrupted memory
• You ask why it did something strange — and there’s no easy way to trace back the decision path

This isn’t just an annoying bug. It’s an integrity failure — and in systems that operate autonomously, it’s a serious one.

What We Don’t Have (Yet)

Unlike confidentiality and availability, integrity doesn’t have clear guardrails in AI systems:

• No standard way to track how an agent’s memory was updated
• No built-in checks to make sure actions match intent
• No simple way to log what the agent thought it was doing — or why

Traditional security tooling wasn’t built for this. We’re flying a bit blind.

A Familiar Pattern: Stuxnet vs. Prompt Injection

If this sounds abstract, think about Stuxnet — a malware that sabotaged physical equipment while pretending everything was normal. It was an integrity attack: the system still worked, but it worked wrong.

Now imagine that same kind of attack, but with a language-based AI agent. Through prompt injection — basically tricking the system with a cleverly worded message — you can plant ideas in its head. And if that agent stores memory, those ideas can stick around and influence future behavior.

This isn’t theoretical. We’re already seeing early examples.

The CIA Triad Revisited

We’re not saying confidentiality and availability don’t matter — they still do, especially since AI agents often handle sensitive data and support important workflows.

But integrity — making sure the system’s behavior remains consistent, predictable, and trustworthy — has to become a first-class priority.

Wrapping It Up

AI agents bring huge potential, but they also introduce new types of risk — especially when they start thinking and acting over time. If we don’t keep a close eye on what they remember, how they make decisions, and whether their behavior has been influenced, we’ll miss the next wave of security failures.

It’s time to stop treating integrity like the “nice-to-have” third pillar. In an AI-powered world, it might be the one that matters most.