AI agent intent: how to tell a mistake from an attack

Imagine an AI agent in your finance workflow accessing the entire payroll table. No one asked it to do this. But if the table is stored in a folder that everyone in the company can access, the agent is simply using its permissions. That is an access hygiene problem. If the agent gains access using a more privileged identity or by having another agent escalate, you have a different problem. Same log line. Same data. Two different responses.

This is the AI agent intent problem: the log records only the outcome, not the intent or the path taken.

An access log alone cannot explain an agent’s actions

Security teams used to focus on user intent, watching for signs of malicious behavior. With generative AI, the focus shifted to analyzing prompts and checking if someone tried to sneak in risky requests. Now, with AI agents, neither approach is enough.

When you give an agent a goal, the system decides which tools and data to use, and in what order. If something goes wrong, looking at the original prompt is not enough, and rarely explains what exactly happened.

Forrester’s report on securing intent notes that controls focused on the user and the prompt miss what happens inside the reasoning loop, memory, and tool calls. When teams treat every incident as a technical failure, they ignore motive. This leads to overreaction to accidents and missed signs of deliberate abuse. Logging has the same blind spot. Conventional logs capture the first prompt and the final response but miss the steps in between. Retrievals, database queries, and tool calls often go unrecorded.

The report suggests treating intent as a distinct security domain. This means, tracking not just what an agent is told to do, but how it chooses actions over time, based on objectives, constraints, and real-world signals (e.g. tool use, data movement, and exceptions). This means intent is not a one-time setting at deployment and teams need to monitor intent continuously as agents operate.

Forrester also draws a line between task and goal:

• Task adherence checks if the agent followed instructions.
• Goal adherence checks if the outcome actually met the user's needs.

For example, if you ask an agent to summarize a document, did it deliver what you needed to make a decision? If all you get is a summary of metadata and file lineage, congrats, the agent technically did the job. But you still have no clue what to decide = The task is complete, but the goal is not met.

Forrester’s Intent Impact Matrix turns AI agent intent into four responses

The report's answer to the payroll question is a sorting tool. The Intent Impact Matrix classifies an agent's behavior along two axes: intentional vs. unintentional and benign vs. harmful, creating four quadrants. This gives security teams a different response for each of the four results to the following scenarios: 

(Find more on Figure 3 - Intent Impact Matrix For Classifying Agent Behavior)

Each finding then goes to its accountable owner, so the response fits the motive, and the program can trust agents without rubber-stamping or blocking everything.

Placing AI agent intent in a quadrant takes five layers of evidence

Before selecting a quadrant, compare the evidence across Forrester’s five layers of intent:

1. Maker intent describes what the agent was designed and approved to do.
2. Organizational intent captures the business purpose behind its deployment.
3. Role-based intent defines what a user in that role should be able to request.
4. User intent describes what the person wants to accomplish.
5. Agent intent concerns the action path the agent selects.

The report gives an example of an account executive requesting deal history before a call. The agent selects an endpoint that also retrieves compensation data, beyond its approved CRM scope. Comparing the request, role, approved design, and tool calls helps locate the deviation without assuming the user acted maliciously.

Data and identity context make the evidence useful

When you investigate agent activity, start by pulling the list of data the agent touched, its sensitivity, and which identities and permissions were in play. Data classification is what lets you tell a routine customer lookup from someone reaching into an HR table they should not see. Remember that prompts and traces can contain sensitive details, so you need to lock down access to the investigation evidence itself.

Cyera Agent Guardian connects agents with their identities, data, and access paths. Its runtime controls evaluate tool invocations and data access against policies, with options to alert, quarantine agents, or block dangerous actions.

The payroll alert begins the investigation. The task, action path, permissions, and data context help determine the response.

Frequently asked questions: AI agent intent

What is AI agent intent?

AI agent intent describes how an agent pursues an assigned objective within its constraints. Security teams assess intent by examining whether the actions the agent takes align with that objective and respect those constraints.

What is intent drift?

Intent drift describes behavior moving away from an agent’s approved mission over time.

How do task adherence and goal adherence differ?

Task adherence concerns whether an agent followed its instructions. Goal adherence concerns whether the result served the person’s intended purpose.

What does the Intent Impact Matrix help teams decide?

The matrix helps teams match their response to the behavior and its consequences. It distinguishes approved helpful behavior, unexpected helpful behavior, accidental harm, and purposeful harm. Those categories support different actions, from reinforcing a safe pattern to containing abuse.

Why does identity context matter?

Identity context helps establish whose permissions an agent used and whether those permissions fit its assigned role. 

Read Forrester’s Securing Intent report for the full framework and recommended capabilities, or book a demo to learn more about Cyera Agent Guardian.

Sources: Forrester Research, Securing Intent: A New Security Domain For Agentic AI, Vision Report, August 2026.

https://reprint.forrester.com/reports/securing-intent-78cf2b3f/index.html