Topic: In this two-part series I will be exploring the application of agentic AI in the cybersecurity domain. In Part 1, we’ll define what agentic AI actually means and map out where it belongs in your security stack. Part 2 will tackle the harder questions of evaluation and implementation.

Core Questions:

• What is agentic AI and how is it different from LLM-based copilots?
• What tasks in cybersecurity are suitable (or dangerous) for autonomous agents?
• How do we evaluate these systems when outcomes emerge over time, not in single outputs?
• What design principles are needed to manage agent behavior, escalation, and learning loops?

Okay Okay…I know. You’re probably rolling your eyes at the mention of LLMs and AI for cyber. We’ve all sat through enough vendor pitches promising that their “AI-powered” tool will solve every security problem known to humanity. The reality is that a chatbot slapped onto a SIEM dashboard isn’t moving the needle in any appreciable way and, quite frankly, is an insult to our intelligence.

However. The more I explore the applications of agentic AI in cyber, the more convinced I am that it has the potential to fundamentally change how we think about security operations. We’re shifting from AI that suggests what you should do to AI that actually does things. The question isn’t whether this technology is coming (it’s already here), but whether we can build these systems thoughtfully enough to handle the speed of modern threats without creating new (catastrophic) failure modes.

So let’s dig into what agentic AI actually means in practice, where it belongs (and where it absolutely doesn’t), and how we can build these systems without shooting ourselves in the foot.

Defining Agentic AI in Security Context

So what exactly makes AI “agentic,” and why should we care about the distinction? If you’ve been in cybersecurity for more than five minutes, you’ve probably noticed that every vendor claims their product is “AI-powered” or “intelligent.” Most of the time, what they’re really selling you is pattern matching with extra steps. True agentic AI is different. It’s like the difference between having a super smart intern who can spot problems and having an experienced analyst who can actually fix them.

A. Beyond the Copilot: What Makes AI “Agentic”

Let’s break this down further. Here is how I classify something as “agentic”:

1. Autonomy means the system makes decisions and takes actions without waiting for human approval. Your typical “AI” security tool sends you an alert and waits. An agentic system sees the same suspicious activity, correlates it with threat intelligence, checks if it matches known attack patterns, and automatically isolates the affected endpoint.
2. Goal-oriented behavior is about working toward objectives rather than just responding to individual prompts. Instead of answering “Is this IP malicious?” an agentic system is thinking “How do I reduce the organization’s exposure to this threat campaign?
3. Environmental interaction means the system can actually change things in your environment, not just observe and report. It can modify firewall rules, revoke access tokens, or trigger incident response playbooks. This is where things get both exciting and terrifying!
4. Temporal reasoning allows the system to plan across multiple time horizons. It might deploy long-term monitoring for a suspected APT while simultaneously taking immediate containment actions. It understands that some threats require patience and others require speed.
5. Learning and adaptation means the system modifies its behavior based on outcomes. If its initial response to a particular type of incident didn’t work well, it adjusts its approach for next time. This isn’t just updating detection rules but rather it’s evolving its entire decision-making process.

B. The Copilot vs. Agent Spectrum

In practice, most security AI exists somewhere on a spectrum rather than falling neatly into categories.

• Copilots are your traditional “AI assistants.” They’re human-in-the-loop systems that excel at analysis and recommendations but require human approval for any actions. Think of ChatGPT helping you write incident response procedures or a tool that flags suspicious network traffic and explains why it’s concerning. These systems are safe but limited by human response time.
• Semi-autonomous agents can take predefined actions within carefully controlled boundaries. They might automatically block known-bad IPs, quarantine suspicious files, or escalate high-priority alerts to the right team members. There’s still human oversight, but it’s exception-based rather than approval-based. Most current “automated” security tools fall into this category.
• Fully autonomous agents operate independently within their defined scope, reporting back to humans primarily for strategic decisions or when they encounter situations outside their parameters. They’re making judgment calls, adapting their tactics, and coordinating complex multi-step responses without human intervention.

Key insight: the value (and risk) increases dramatically as you move along this spectrum. A copilot that misunderstands a threat scenario wastes your time. An autonomous agent that misunderstands the same scenario might lock out half your user base or miss a critical attack while chasing false positives.

The sweet spot for most organizations right now is in semi-autonomous agents with really good escalation logic and transparent decision-making processes. This may change as the technology matures and threat landscapes evolve.

Now that we’ve established what makes AI agentic, let’s think about deployment.

III. Mapping the Cybersecurity Agent Landscape

Ok, so…where should we actually deploy these autonomous agents, and where should we absolutely not? This isn’t just about technical capability. It’s about understanding the blast radius of getting it wrong.

A. The Sweet Spots: High-Value Use Cases

These are the areas where agents can add real value with minimal harm.

Threat hunting and investigation is probably the lowest-hanging fruit. Agents excel at sifting through massive datasets, correlating seemingly unrelated events, and following investigation threads that would take human analysts hours or days. They can simultaneously query multiple data sources, apply complex logic patterns, and maintain context across long-running investigations. The worst-case scenario? They waste some compute cycles chasing false leads.

Incident response orchestration is where agents really shine. This involves coordinating multiple tools, triggering the right playbooks, and ensuring nothing falls through the cracks during high-stress situations. They can simultaneously gather evidence, notify stakeholders, coordinate with external teams, and execute containment procedures while maintaining detailed audit logs. Human analysts can focus on the strategic decisions while agents handle the tactical execution.

Vulnerability management has become an impossible human-scale problem. Agents can continuously assess, prioritize, and even auto-remediate certain classes of vulnerabilities based on threat intelligence, asset criticality, and organizational risk tolerance. They can coordinate patching schedules, validate fixes, and track remediation progress across complex environments without the usual human bottlenecks.

SOC automation for alert triage is another natural fit. Most security alerts are false positives or low-priority events that follow predictable patterns. Agents can handle the initial triage, enrich alerts with contextual information, escalate genuine threats to human analysts, and automatically close obvious false positives. This isn’t about replacing SOC analysts. It’s about letting them focus on the interesting problems.

Compliance monitoring and policy enforcement are perfect for agents because they require consistent application of well-defined rules across large, dynamic environments. Agents can continuously monitor configuration drift, flag compliance violations, and even auto-remediate certain policy deviations without human intervention.

Threat intelligence synthesis is another area where agents add clear value. They can aggregate feeds from multiple sources, correlate indicators, assess credibility, and produce actionable intelligence summaries. They’re not making strategic decisions about threat actor attribution, but they’re doing the heavy lifting of data processing and initial analysis.

B. The Danger Zone: Where Agents Should Not Go (There Be Dragons!!)

Some areas are just way too risky for autonomous operation, regardless of how sophisticated the agent. Do not pass Go, do not collect $200.

Network segmentation changes can isolate critical systems or disrupt business operations in ways that might not be immediately obvious. An agent might correctly identify a compromised system but cause more damage by cutting off dependent services. The interconnectedness of modern networks means that seemingly simple changes can have cascading effects.

User access revocation requires nuance that agents currently lack. Yes, that login from a suspicious location might look malicious, but it could also be your CEO traveling internationally on a critical deal. The business impact of incorrectly blocking legitimate users often outweighs the security benefit of automated access control.

Incident attribution and legal actions require human judgment, institutional knowledge, and understanding of business relationships. Agents might correctly identify technical indicators of compromise, but they can’t understand the political implications of pointing fingers at specific threat actors or the legal requirements for evidence handling.

Policy creation and modification should remain firmly in human hands. While agents can flag policy violations and suggest updates based on observed patterns, the creation of security policies requires understanding of business context, regulatory requirements, and organizational culture that agents don’t possess.

Cross-system architecture changes carry too much risk of unintended consequences. An agent might correctly identify that moving a service would improve security posture, but it probably doesn’t understand the downstream impacts on performance, compliance, or user experience.

Evidence handling for forensics has strict chain-of-custody requirements that automated systems can’t currently meet. While agents can assist with evidence collection and initial analysis, the legal requirements for forensic evidence typically require human oversight and attestation.

C. The Gray Area: Context-Dependent Applications

Some use cases fall into a gray area where the answer depends heavily on your organization’s risk tolerance and operational maturity.

Automated blocking and quarantine is the classic example. In some environments, false positives are annoying but manageable. In others, blocking the wrong traffic could shut down critical business processes. The key is understanding your environment well enough to know where you can afford to be aggressive and where you need to err on the side of caution.

Security awareness training personalization can be valuable, but it raises privacy concerns about how much behavioral data you’re comfortable with agents collecting and analyzing about your employees. There’s also the question of whether agents should be making decisions about who needs additional training based on their observed behavior.

Third-party risk assessment automation can save significant time, but it might miss nuances in vendor relationships or contractual obligations that require human judgment. An agent might correctly identify technical risks but fail to account for the business criticality of a particular vendor relationship.

Bottom Line: Agents work best when the problem space is well-defined, the blast radius of mistakes is limited, and the decisions don’t require deep organizational context or business judgment. As we move into murkier territory, the need for human oversight increases dramatically.

Conclusion: Getting Ready for the Agentic Security Era

At this point you’re probably thinking one of two things: either “this sounds promising but terrifying” or “my security team can barely handle our current automation, let alone autonomous agents.” Both reactions are completely valid.

In my opinion, agentic AI in cybersecurity isn’t a question of if, but when and how. The threat landscape is moving too fast for purely human-driven responses, and our adversaries are already experimenting with automated and AI-powered attacks.

The practical guidance I’ve covered here gives you a mental model for thinking about where agents belong in your security operations. Start with the high-value, low-risk use cases like threat hunting and alert triage. Build your organizational muscle around agent oversight and escalation. And for the love of all that is holy, stay away from the danger zones until you’ve mastered the basics.

But understanding what agents can do and where they belong is only half the battle. We still have to tackle the harder questions: How do you evaluate systems that learn and evolve? How do you build trust in decision-making processes you can’t fully predict? And how do you maintain human agency in an increasingly automated security environment?

That’s what we’ll tackle in Part 2. We’ll explore the evaluation challenges, design principles, and the practical steps for implementing these systems without breaking everything. Stay tuned!