Many enterprises have deployed AI agents that have credentials, can access systems, make decisions, and act. Fewer can say who authorized them, what systems they can reach, or what specific functions they performed at any given time. They often act without human intervention at a speed no person can keep up with.
That’s because most AI agents are governed by security frameworks designed for traditional software, which has fixed permissions and functions predictably.
A new report from ServiceNow on the state of agentic security notes, “Existing frameworks assume a person makes each decision and can be held to it. Agents break that assumption, and few teams have rebuilt the model for how agents operate.”
Autonomous security is the emerging solution for AI software that’s constantly acting on something, with continuous visibility into what AI agents are doing.
Autonomous security means giving AI agents identity and access controls that catch problems before, not after, they cause damage. It's built into how agents sense what's happening, decide what to do, act, and stay secured at every step across the entire business.
That degree of governance is what many AI programs lack when they stall in the pilot phase.
Leaders aren't convinced AI agents can be trusted to do important work unsupervised. According to IDC, “security, privacy, and governance concerns ranked as the single most common challenge for buyers adopting AI-driven intelligence solutions, ahead of budget, skills, and technical integration.”
The research and advisory firm notes that when AI projects fail to move from pilot to production, 27% of organizations blame challenges in protecting sensitive data, and 23% blame inadequate data governance.
An AI agent with the wrong permissions, or one that goes outside its intended scope, can do a lot of damage very fast. Consider an incident Google disclosed in September, where its Gemini AI model escaped its confines during testing and hacked into three companies.
Most organizations deploy AI agents team by team, with no central inventory of what each one can touch. There's rarely one place to manage them all. That makes it difficult, if not impossible, to know what an AI agent can access, what permissions it has, or where risk is lurking.
Agentic AI multiplies the attack surface (the total number of ways attackers can get in) because each agent carries its own credentials and connections to other systems. But it compresses the time it takes to notice and respond.
Legacy security systems assume that once a user or program is authenticated, everything it does afterward is legitimate. That assumption fails with AI agents. The OWASP agentic risk framework highlights that an agent can use valid credentials to carry out harmful instructions hidden in an email or document. To traditional security systems, it looks like business as usual.
Answering "Does this behavior match what this agent is supposed to do?" requires knowing three things:
- The AI agent's identity and permissions
- What the AI agent has already touched
- What the AI agent is doing right now
In most organizations, those three things live in different systems that don't talk to each other. By the time someone pieces together the picture, the AI agent has already acted, because the data needed to build the full picture has never lived in one place.
Fixing this means moving past one-time access checks and building real-time guardrails around what an AI agent is doing. Enterprises that have this visibility can identify and contain problems faster.
Legacy software security uses set rules and humans to spot and fix problems after a breach happens. Autonomous security uses AI agents to monitor systems and catch problems automatically before they cause harm. That’s a very different approach to security, one that requires teams to fundamentally rethink their defense strategy.
ServiceNow refers to this as Shift Zero. It’s the idea that instead of waiting for a threat to land, you stop it before it gets off the ground.
ServiceNow’s agentic security report lays out five steps to building such a defensive security stance in the AI era.
AI agents can multiply fast, brought online by different teams for different tasks, often without security or IT even knowing they exist. This means you need an always-updating list of every AI agent running in the business, a map of which systems and data each one can touch and which actions they can take.
Security can't be something bolted on after an AI agent is already live and doing work. It has to be part of how the agent gets built from the start, with permissions, guardrails, and scope well defined up front.
Many organizations end up with security data scattered across tools, with different systems tracking different AI agent activities. Those systems often don't talk to each other. This guideline is about pulling visibility and management into one place so that a security team can see how an agent's identity, permissions, and risk level connect.
Periodic audits, such as checking in once a quarter or after an incident, leave big blind spots where nobody knows what an AI agent did. True visibility requires logging AI agent behavior and decisions continuously, so there's always an up-to-date, ongoing record of what happened, when it happened, and under whose authority.
Every AI agent needs a dedicated person responsible for it, just as every employee has a manager. That person owns what the AI agent is allowed to do, answers for what it does, and gets looped in when a decision needs to be made.
Consider a scenario where a hacker gets access to a login for a customer-facing AI service tool.
With traditional security, the login is the checkpoint. Once a hacker slips past it, the system assumes it’s legitimate. Once inside, an AI agent can access customer records or other sensitive information. This goes undetected (at least, for a time) because the system thinks the agent is doing its job. A hack might not be discovered until an irate customer calls.
With autonomous security, the login is just the first step. The system watches what the AI agent does once inside, measured against what it’s supposed to do. For example, if the agent had never accessed order status data before but suddenly accesses payment data it never touched, this misalignment would get flagged and a kill switch would stop further action immediately.
You may be thinking “Isn’t autonomous security itself AI making important judgment calls? Who’s keeping tabs on that?” Autonomous security systems are built with the same scoped permissions and audit trails that they enforce on other AI agents. So, the security system shows up in the same agent inventory as what it's watching.
Every employee has a manager who answers for what they do. With autonomous security, so does every AI agent.
That’s what gives enterprises the trust they need to scale agentic AI, with faster containment of rogue agents, fewer blind spots, and greater accountability (whether human or AI) at all levels.
Find out how ServiceNow can help you monitor and contain AI agents.
