↓Skip to main content

Beyond the Hype: What 'Agentic AI' Actually Means for a Security Analyst

·5 mins·
AI Security Strategy
Naol Mengistu
Author
Naol Mengistu
Cybersecurity Engineer & Researcher
Table of Contents

If you work in tech, you’ve heard the term: Agentic AI. It’s the latest buzzword to follow on the heels of generative AI, promising a future of autonomous systems that don’t just talk, but do. The hype paints a picture of AI agents independently running companies, discovering scientific breakthroughs, and, in our world, single-handedly defending entire networks from attack.

As a security professional who spends time in the trenches, building SOC labs, analyzing logs, and studying threat patterns, my first reaction is always a healthy dose of skepticism. We’ve been promised silver bullets before.

But agentic AI is different. It’s not just another chatbot with a better vocabulary. It represents a fundamental shift in how we interact with machines. And for the overworked, alert-fatigued security analyst, it’s a change we desperately need to understand. To cut through the hype, we need to ask a simple question: what is an AI agent, and what will it actually do in a real-world Security Operations Center (SOC)?

First, What It’s NOT: A Better ChatGPT
#

Let’s clear up a common misconception. A generative AI model like ChatGPT is a powerful information processor. You give it a prompt, and it gives you a well-reasoned, articulate response. It can write code, summarize text, or explain complex concepts.

Think of it as a brilliant librarian. It has access to vast knowledge and can synthesize it for you on command.

An AI agent, however, is more like a junior research assistant. You don’t just ask it for information; you give it a goal. The agent then autonomously breaks that goal down into steps, uses tools to execute those steps, observes the results, and adjusts its plan until the goal is achieved.

The key components that make an AI “agentic” are:

  1. Goal-Orientation: It starts with an objective, not just a prompt. (e.g., “Investigate this security alert and determine if it’s a true positive.”)
  2. Autonomous Planning: It can create a multi-step plan to achieve its goal. (e.g., “1. Analyze IP address. 2. Check user login history. 3. Correlate with firewall logs.”)
  3. Tool Use: This is the most crucial part. An agent can interact with the outside world by calling APIs, running scripts, or querying databases. It can act.
  4. Self-Correction: It can review the outcome of its actions and decide on a new course if the first one fails.

This ability to use tools is what separates it from a simple chatbot. It doesn’t just tell you how to check an IP’s reputation; it actually calls the VirusTotal API and gets the result itself.

A Day in the Life: The Analyst and The Agent
#

Let’s imagine a common scenario in any SOC: an alert fires in the SIEM.

Alert: “Impossible Travel: User ‘j.doe’ logged in from Norway at 09:00 and from a suspicious IP in Brazil at 09:05.”

Today’s Human Analyst Workflow (The Manual Grind):

  1. Acknowledge the alert in the ticketing system.
  2. Copy the suspicious IP address.
  3. Open a new browser tab, go to VirusTotal or another threat intel site, and paste the IP. Analyze the results.
  4. Open another tab, go to the SIEM, and run a query for all activity from that IP in the last 24 hours.
  5. Run another query for all of ‘j.doe’s’ login activity in the last 24 hours.
  6. Check if ‘j.doe’ is on a known travel list or has a company VPN registered to that IP block.
  7. Synthesize all this information.
  8. Write up a summary in the ticket and decide whether to escalate to a senior analyst.

This process involves a dozen manual steps, takes 15-30 minutes, and is one of a hundred similar tickets that day.

Tomorrow’s Agentic AI Workflow (The Augmented Analyst):

  1. The alert fires. The SIEM automatically triggers an AI agent with a single goal: “Investigate and summarize this Impossible Travel alert for j.doe.”
  2. The Agent’s Autonomous Plan:
    • Step 1: Enrich IP. The agent calls the VirusTotal API with the Brazilian IP. The API returns a high malice score.
    • Step 2: Correlate Activity. The agent queries the SIEM for all logs associated with both the IP and the user ‘j.doe’. It finds firewall drops and successful authentication.
    • Step 3: Check Internal Context. The agent queries the internal HR database API. It finds that ‘j.doe’ is not on approved travel.
    • Step 4: Summarize Findings. The agent compiles all this information into a structured summary.
  3. The Result: The human analyst receives a notification five minutes later. The ticket is already populated with a concise summary:

    “True Positive Alert: User ‘j.doe’ login from Brazil IP (1.2.3.4) confirmed malicious (VirusTotal Score: 89/90). No authorized travel. Recommend immediate account suspension and escalation to Tier 2.”

The Analyst’s New Job Description
#

Notice what happened. The agent didn’t replace the analyst. It performed the repetitive, time-consuming Tier 1 triage with superhuman speed and accuracy. It took over the “grunt work.”

This frees the human analyst to become what they were always meant to be: a true investigator. Their job is no longer to manually gather data but to make high-level judgments based on the rich summary the AI provides. They shift from asking “What happened?” to asking “What do we do next?” and “How do we prevent this from happening again?”

The analyst’s role is elevated. They become the manager of a team of tireless AI assistants, directing them, interpreting their findings, and focusing their own formidable human intuition on the truly novel and complex threats that the AI can’t yet understand.

Agentic AI isn’t the end of the security analyst. It’s the end of the analyst as a human data-gatherer. It’s the beginning of the analyst as a true strategic defender. And that’s a future worth getting excited about.