Agentic AI

AI that pursues a goal across several steps and takes actions on its own, instead of answering one prompt at a time. In security it lets investigation and response run autonomously, within guardrails set by the team.

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Agentic AI is artificial intelligence that pursues a goal across several steps and takes actions on its own, instead of answering one prompt at a time. An agent can plan, use tools, check results and adjust, within limits set by the people who deploy it.

How is agentic AI different from a copilot?

A copilot is started by a person with a prompt. It suggests and summarises, and the person drives each step. Agentic AI is started by the system from a goal. It plans and acts, while people set the limits and supervise.

A copilot helps a person work faster. An agent carries the process through.

What is the difference between agentic AI and automation?

Classic automation, such as a playbook, follows fixed rules written in advance. It is reliable for predictable cases. Agentic AI decides the next step based on the situation, which makes it suited to investigations where every case is different.

What can agentic AI do in security operations?

It can take over work that is repetitive and high in volume. In identity security that includes investigating an alert by collecting an account’s privileges, recent activity and related events, prioritising findings by risk, containing a threat with an approved action and documenting what was found and done.

How does an agentic process work?

  1. Trigger: a signal or alert starts the process.
  2. Gather context: the agent collects privileges, recent activity and related events.
  3. Assess: it compares the behaviour with what is normal and judges the risk.
  4. Act: it takes an approved action, such as limiting a session.
  5. Report: it records the steps and the reasoning, so people can review them.

Why does agentic AI matter for identity security?

Identity attacks use valid accounts, so they blend in, and every hour of delay gives the attacker more room to move. Autonomous investigation starts when the signal appears, not when an analyst is free.

An example: an unusual query appears on a critical application from a privileged account. An agent gathers the account’s activity, compares it with what is normal, judges the case high risk, limits the session and sends the full record to an analyst for review. The routine work is done before anyone opens the alert.

What does agentic AI need to work well?

Good context. An agent is only as useful as what it can see: identity, privileges, behaviour and activity in one place. Fragmented tools lead to fragmented decisions.

Is an AI agent an identity?

Yes. An agent holds credentials and access like any other account, so it is a non-human identity. It needs an owner, a narrow scope and monitoring, in the same way as a service account.

What are the risks of agentic AI?

  • Overbroad access: agents with more permissions than the task needs.
  • Opaque decisions: actions that cannot be explained afterwards.
  • Unsupervised high-impact actions: autonomy without approval where it matters.
  • Unmonitored agent identities: credentials that nobody reviews.

How much autonomy is right?

It is not all or nothing. Some processes can run fully on their own, while others should propose an action and wait for approval. A good design lets the team decide, process by process, and change it as trust grows.

Does agentic AI replace analysts?

No. It takes over routine work so analysts can focus on judgement, complex cases and oversight. The job changes from working every alert to supervising a process and handling what does not fit the pattern.

How do you measure success?

Look at the time to investigate, the time to respond, analyst workload and the share of cases handled without escalation. Compare them before and after, and review a sample of automated decisions regularly.

How do you keep agentic AI under control?

Define guardrails and approval thresholds before switching autonomy on, keep a full record of every step, keep people on high-impact decisions and treat agent identities like any other identity.

Sharelock is an AI-native identity security platform built around agentic processes that act autonomously within those limits, so people supervise outcomes instead of driving each step.

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