What Is AI Security?
AI security is the set of practices, controls, and technologies that protect an organization from the risks introduced by its use of AI, whether that AI was adopted by an employee, built by an engineering team, or turned loose as an autonomous agent.
AI security is hard to define because the thing it's defining refuses to sit still.
First it meant stopping people from pasting sensitive data into ChatGPT. Then it meant securing whatever homegrown app or chatbot engineering had bolted a model onto. Now agents that call tools and run on standing credentials are rewriting the risk model again, and there's no reason to think that's the last rewrite.
That's the real problem with wanting a tidy, one-line definition: it goes stale the moment the technology moves, and the technology never stops moving. What doesn't move nearly as often is where the risk actually lands.
The three surfaces of AI security
The risk breaks down into three areas, and almost every organization divides along the same lines: who's using AI, what's being built with it, and what agents are doing without anyone in the loop.
How people use AI. Employees, contractors, vendors, all reaching for AI to get work done faster: consumer chatbots, AI-powered code assistants wired straight into engineering workflows, browser extensions and plugins nobody inventoried. The risk here isn't exotic. It's visibility. You can't govern what you can't see, and most organizations are running a lot more AI inside their walls than anyone in security would guess. This is where shadow AI (tools adopted with no approval from IT or security) and sensitive data leaking out through user prompts both live.
What you build with AI. Your engineering team has already wired a large language model (LLM) into a customer support bot, an internal knowledge assistant, or a homegrown app with real users, whether or not security signed off on it. Every one of those integrations comes with a class of vulnerabilities traditional software never had to deal with: prompt injection, data leaking out through model outputs, plugins and tools with more trust than they've earned, toxic or harmful output landing in front of a customer.
What your AI agents do when you're not watching. The newest surface, and the least mature. Agents that call tools, chain actions, and run on standing credentials introduce risks with no clean precedent, starting with the fact that most of them are carrying more access than any single employee would ever be granted. Few organizations have built an identity system for a non-human actor operating at machine speed, so a lot of agents end up over-privileged by default rather than by decision. A compromised or malicious Model Context Protocol (MCP) server can poison the tools an agent trusts. Privilege escalation through action chaining, an agent quietly doing more than it was ever authorized to, is a documented failure mode now, not a thought experiment.
These three don't stay in their lanes. An internal chatbot built on a foundation model is something your team built, something employees will ask questions it was never designed to answer, and, if it can take action on someone's behalf, an agent with its own risk profile, all at once. Treat this as one blob of a problem and programs stall. Treat it as only two problems (usage and everything else) and the agentic layer gets bolted on as an afterthought instead of designed for from day one.
Why traditional security tools don't cover AI security risks
Most existing security tooling, data loss prevention (DLP), cloud access security brokers (CASB), network filtering, was built on an assumption that no longer holds: software behaves deterministically. Same input, same output, every time. LLMs don't work that way. Feed the same prompt in twice and you can get two different answers back, and a control built for fixed inputs and outputs will miss the failure modes that matter.
That doesn't mean the old tools are useless. Blocking known-bad domains still catches things. It means they're not enough on their own, and treating an AI security problem like a slightly bigger web filtering problem is how gaps get built into the foundation.
None of this is hypothetical. Incidents involving indirect prompt injection in mainstream AI assistants (the EchoLeak vulnerability in Microsoft Copilot is the clearest public example) have already shown that a document or an email, not a user, can be the attack vector. The OWASP Top 10 for LLM Applications keeps adding agentic risk categories for the same reason: the industry is still rewriting its own threat model as the technology moves.
One data point changes the whole picture: analysts including Gartner have estimated that most unauthorized AI activity inside organizations traces back to internal policy violations, oversharing, and misuse, not external attackers. The gap most security teams are fighting isn't a missing piece of technology. It's governance that hasn't caught up to how fast usage grew. The clearest symptom is the audit trail: most AI deployments can't produce a log detailed enough to reconstruct what an agent or application actually did, and that's a real problem the moment anyone outside the security team asks for it.
How to evaluate AI security vendors
"AI security" gets slapped on products doing genuinely different things, and vendors aren't in a hurry to clear that up. Some are using AI to make traditional detection and response better, which is a different category entirely from securing AI systems themselves. Network and DLP vendors are bolting AI features onto what they already had, and some of it works, but a static URL filter was never going to hold up against a threat model built around manipulating model behavior.
The fastest way through the noise: ask which of the three surfaces above a product covers. Most vendors are strong in one lane. Few genuinely cover all three, and a pitch that claims otherwise won't survive a direct question about which surface it was built for first.
What an AI security program looks like
Every vendor pitches this differently. Mature AI security programs, though, converge on the same handful of practices, no matter which of the three surfaces they're covering. It starts with inventory and visibility: you can't secure what you don't know exists.
From there, policy that people can actually follow matters more than policy that looks good in a slide deck. Blanket bans just push usage further into the shadows, while clear, tool-agnostic guidance on data handling outlasts an approved-tools list that goes stale within a quarter.
Testing has to happen before deployment, not after. Homegrown apps and agents should get tested against real attack scenarios, prompt injection, jailbreaks, data poisoning, before they ship. That testing needs to feed directly into runtime enforcement rather than sit next to it: the best programs treat the two as one loop, where whatever a red-teaming pass turns up becomes a production guardrail automatically, not a ticket that gets re-implemented three sprints later. Either way, policy has to hold where the traffic actually flows, not just live in a wiki nobody opens.
The two pieces most programs skip: incident response built for how AI fails, since unusual model or agent behavior looks nothing like a compromised endpoint and most playbooks haven't been rewritten since AI adoption, and a way to shut a misbehaving model or agent off without taking down the entire workflow it's embedded in. That second gap gets more expensive to leave open the deeper AI gets built into core processes.
None of this is a box you check once. All three surfaces move fast enough that "we handled AI security last year" is stale before it's even finished being said.
Bottom line
AI security isn't a checklist item or a category you can buy your way out of. It's the ongoing work of defending three specific surfaces, usage, what gets built, and what agents do without supervision, no matter which vendor, model, or attack technique happens to be sitting on top of them this quarter. Get those three right and everything else is maintenance. Get them wrong and every new tool you add just papers over the same gap.
Prompt Security, now part of SentinelOne, secures AI wherever it runs. Whether it's employee usage, apps you're building, or AI agents that your team is running. Learn more about the platform here.
