“Don’t be afraid, our robot overlords might just be better than the current ones.” I opened a recent talk with that tongue-in-cheek line, and while it got a chuckle, there’s a serious point behind it. As security professionals, we’ve grown accustomed to a parade of “next big things.” Cybersecurity is littered with miracle cures that never quite deliver, and I’m usually the first to roll my eyes when a new one shows up.
But agentic AI is something different. It’s not just another gadget or software update, it’s a fundamental shift in how we might defend (and attack) in cyberspace. In this post, I want to unpack why agentic AI represents a true leap in the evolution of cybersecurity tools, not a mere step, and how we as security leaders can harness it safely.
From Automation to Autonomy
We’ve all seen AI hype come and go. So what makes agentic AI special? In a word: autonomy. Note to be clear current offerings do not offer autonomy. Upcoming studies show that much of what is being sold as AI agents today are essentially ‘workflow puppets’ without true autonomy. Traditional AI, including many “smart” security tools, are like trained specialists: they do one thing (say, detect malware or triage an alert) very well under preset conditions. Agentic AI, on the other hand, is more like a junior analyst with a mind of its own. It combines reasoning, memory, and the ability to act, enabling it to plan and make decisions in pursuit of a goal without constant human micromanagement. In practical terms, an agentic AI might use a large language model as its brain, retain state from past interactions, and dynamically interact with its environment or other tools. Research prototypes like AutoGPT and OpenAI’s function-enabled agents have shown early glimpses of agentic behavior – combining reasoning, memory, and external action – though most remain in experimental or highly controlled environments. It’s the difference between a chatbot that can answer questions versus an AI agent that can analyze data, decide a course of action, and execute it by calling APIs, running scripts, or even interacting with other systems or humans to achieve an objective.
To put it simply: agentic AI moves us from automation (following a script) closer to autonomy (figuring out the script as it goes). This leap allows AI to handle complex, multi-step tasks in real-time. For example, instead of a static program checking if a file is malware, imagine an AI agent that can detect a suspicious file, investigate it (consult threat intel sources, run it in a sandbox, gather context), and then take action (quarantine it, alert staff, initiate further hunting), all in one workflow. And it does this not because we predefined that exact sequence, but because it “figured out” what steps are needed to meet the goal of protecting a specific system.
A True Evolutionary Leap (Not Just a Buzzword)
Let’s step back for some perspective. In the history of human progress, we’ve seen a few inflection points where everything changed: the cognitive revolution, the agricultural revolution, the scientific revolution, and so on. In technology, we had the personal computing boom, the internet’s birth, the mobile revolution, and the AI/machine learning surge. Each time, our tools evolved from aiding humans in basic tasks to empowering us in ways previously unimaginable. I’d argue that agentic AI is shaping up to be one of those leaps.
Think of what’s already happening in medical imaging. Recent studies on mammograms and brain tumor scans demonstrate that when a domain is saturated with high-quality data, conventional AI systems can start to approach human specialists. That result is a small but important perspective of the philosophical view of Dataism: once any process—biological or digital—can be reduced to dense data flows, algorithms begin to dominate. Today these systems rely on supervised learning and require a human to act on their findings, but they preview the next step.
We can think similarly about the evolution of security operations over the past decades. In the early days, we manually parsed logs and chased hackers with very rudimentary tools. Then came SIEMs (Security Information and Event Management systems) aggregating our data and correlating events – a necessity as scale grew. Later, we added playbooks and SOAR (Security Orchestration, Automation, and Response) to automate responses to common threats. No doubt these were big improvements. But even the best playbook is essentially a checklist written by a human; it doesn’t think, it just executes. Agentic AI has the potential to go beyond that, to handle unexpected situations that our static runbooks don’t cover. Instead of an automated script that resets a user’s password when certain alerts fire, imagine, in the near future, agentic AI that could be capable of assisting across the full incident lifecycle–from detection to remediation–with appropriate safeguards and human oversight.
That level of initiative is unprecedented in our security toolkit. It’s the difference between a guard dog that barks when a stranger approaches, and a security guard who can challenge the intruder, check their ID, and call the authorities as needed. It kind of feels like when we first connected computers to the internet–opening up a world of new possibilities and threats at the same time. Agentic AI has that dual nature: immense opportunity and new risk all bundled together. Which brings me to the next point…
The Promise: Smarter, Faster Security
Why am I excited about Agentic AI? Because I’ve seen where our current tools fall short. Our security teams drown in data and alerts. We write playbook after playbook to automate fixes, yet breaches still happen because attackers innovate faster than our scripts. Agentic AI offers a chance to tip the scales.
Picture your Security Operations Center (SOC) at 2:00 AM: the SIEM flags an unusual login pattern that might indicate an attack. Today, either an on-call analyst rubs their eyes and starts pulling data, or maybe you have a basic automation that disables the account pending review. An agentic AI could handle this scenario far more elegantly. It might gather contextual data from multiple systems (VPN logs, HR records, recent support tickets by that user, etc.), determine the anomaly is a false alarm (say the “attack” was just Bob in Sales using a new VPN), and quietly close the alert. Or, if it is malicious, the agent can isolate the user’s sessions, reset credentials, and launch an enterprise-wide hunt for similar activity – all as part of a coherent game plan it devised on the spot. Essentially, you’ve got a digital first responder who can do the tedious work in seconds and even initiate complex response actions that a human would do, but faster. Add to that the ability to run all playbooks on all data all the time and we start seeing where this could go.
And it’s not just within the enterprise. On the fraud prevention side (close to home for us at HUMAN Security), Agentic AI could power commerce bots that help users perform tasks. Imagine a personal shopping agent that can scour the web for the best price on a gadget and purchase it for you, or a travel agent bot that handles your flight bookings. These are the agentic commerce scenarios currently emerging right now in B2C sectors. Tomorrow’s web traffic will include hordes of good bots acting on behalf of users. Security teams need to be ready to let the good guys through while keeping the bad ones out. This shift in thinking – that not all bots are bad – is an important adjustment for security teams. So yes, I’m bullish on the benefits. A properly trained and governed AI agent could take over repetitive Tier-1 support tickets, scour configurations for vulnerabilities at 3 AM, or even serve as an automated penetration tester probing our defenses continuously. But before we get carried away, let’s talk through the potential risks and how we trust these new helpers.
New Risks on the Horizon (and Old Tricks with a Twist)
Every leap in capability comes with a leap in potential abuse. Agentic AI is no exception. Giving software agents the freedom to make decisions and act means they can also screw up or be manipulated into doing something harmful. The security community has already started cataloging the unique threats that come with autonomous agents. OWASP, known for its AppSec guidance, recently published a list of agentic AI-specific threats. It reads like a sci-fi horror show for CISOs:
- Memory Poisoning
- Tool Misuse
- Goal Manipulation
- Identity Impersonation
- Excessive Agency
…and the list goes on (if you’re curious for the full rundown of all 15 threats and their mitigations, check out our detailed breakdown here). The short version: agentic AI introduces new attack surfaces. An autonomous agent can be tricked or subverted in ways a traditional program can’t, precisely because it’s always learning and making decisions. It might hallucinate false information and act on it (cascading hallucination attacks, as OWASP calls it). Or a pack of malicious agents could even collude in unexpected ways inside your environment. Meanwhile, old threats like DDoS or phishing take on a new flavor — imagine phishing not a human, but an AI agent to give up its credentials or control. Or without the proper guardrails that AI analyst can be tricked into creating a massive outage. It’s a strange new world.
As a security leader, I find this exciting. We’re essentially facing a new class of adversaries and a new class of allies at the same time. On one hand, attackers will undoubtedly weaponize agentic AI for things like smarter bots and malware that can adapt and evade. On the other hand, we get new defensive powers with our own AI agents. It’s like a chess game where suddenly new pieces have been added for both sides. To truly leverage agentic AI, we must address these risks head-on.
Building Trust: Verifying and Governing AI Agents
So how do we embrace agentic AI without losing sleep (or losing control)? The answer is a mix of technology, process, and plain old human oversight. We often say in cybersecurity, “trust but verify.” For agentic AI, I’d amend that to “establish trust then verify continuously.”
1. Identity and Authenticity for Agents: In the near future, I believe we’ll treat AI agents much like we treat human users or devices today in terms of identity management. Each agent needs a verifiable identity and a trust level. Who built it? What data was it trained on? Who is it authorized to represent? How can we prevent malicious actors from compromising AI agents, or using illegitimate ones in identity spoofing attacks? Already, Stu Solomen CEO at HUMAN has highlighted the importance of verifying that agentic applications are legitimate and secure. Google has published some recent recommendations.
2. Guardrails and Least Privilege: Just because an agent can call every API doesn’t mean it should. Good security practice will require putting complex guardrails around what tools and systems an agent can access and enforcing the principle of least privilege. Think of it like role-based access control for an AI: if an agent’s job is to manage employee onboarding, it shouldn’t also have free rein over the financial database. Sandboxing agents during testing is also key – let them prove themselves in a controlled environment before unleashing them on production. And if an agent ever tries to step out of bounds (say, our HR onboarding bot suddenly wants to change firewall configs), alarms should go off.
3. Monitoring and Real-Time Oversight: The term “human in the loop” still matters. Even an autonomous agent should report its actions and reasoning (in a form we can audit). We’ll need our systems to log every significant decision an agent makes and every tool invocation it performs. This way, if something goes wrong or just for a routine audit, a human analyst can trace back what the agent did and why. It also helps in developing trust over time – if the agent proves it handles 100 incidents correctly, you might grant it a bit more leeway. However, when an agent makes a questionable move or faces an ambiguous situation, it should either seek human approval or at least flag it for review. In practice, this might mean your SOC platform shows a live feed, like “Agent Alpha is attempting action X based on reasoning Y – [Allow/Deny]?” for critical decisions. The oversight might be lighter for low-risk tasks and tighter for high-risk ones. The goal is not to babysit the AI (defeating the purpose of autonomy) but to coach it and catch misbehavior early.
4. Resilience Against Manipulation: Finally, we have to harden our agentic systems against the new attacks. This means training them to detect when they’re being fed malicious input (like a weird sequence of prompts that looks like an injection attempt) and to fail safe. It also means traditional security controls aren’t going away: we’ll still need firewalls, API security, and validation layers to block an AI agent from doing destructive things if it does get compromised. In effect, we’ll be treating AI agents as another layer in our architecture to secure, with all the defense-in-depth we apply elsewhere. We might even end up scanning our AI’s “brain” (its prompts, memory, policy) for signs of poisoning or tampering, analogous to malware scanning. It’s a fascinating area of R&D right now.
I’m encouraged that industry groups are already on this. The OWASP guidance I mentioned is aimed at developers to bake in security when building agents. And companies like ours are exploring how to extend detection and bot management tech to cover friendly AI agents acting in your environment. It’s early days, but the blueprint is forming: to safely deploy agentic AI treat agents as both valuable team members and potential insider threats.
Looking Ahead: Embracing the Agentic Era (Carefully)
Every revolution in cybersecurity has come with cynics and cheerleaders. With agentic AI, I find myself feeling a bit of both. I’m thrilled by the potential, but I’m also mindful that we have to get this right. An unchecked autonomous agent rampaging through a network will become the next headline none of us want to see. The key will be balancing innovation with governance. We could also be in a bit of a golden era with AI where it has not been co-opted to push specific products or agendas in ways explored in studies like Golden Gate Claude. Transparency and proven trusted stewardship will become increasingly important.
We stand at the cusp of a new world where software agents will negotiate deals, run networks, and yes, fight off attackers. It’s a world that will change the meaning of authenticity online and even what it means to be “human” on the internet. At HUMAN Security, we’re committed to staying at the forefront of both the opportunities and the risks of this agentic AI era. That means investing in the tech to enable authentic and trusted interactions between humans and AI agents and arming our customers with visibility and control over these non-human helpers.
In closing, I’ll paraphrase what I often remind my team: Don’t panic but don’t fall asleep at the wheel either. In this blog, we have talked about directionally where AI may be used in the future but we still need to set it up correctly. We are at the start of our journey here – current efforts in this space will look as archaic as early surgery in the 1800s. Agentic AI may eventually help us cure diseases or solve cyber challenges we’ve struggled with for decades, but it will still rely on us to ask the right questions, set the right guardrails, and build trust.