The Security Bot Just Took Action. Without Asking.

AI agents are already watching your shopfloor. But do you really know what they’re doing and what they might do next?

Jun 17, 2025

Remember 2001: A Space Odyssey?

That moment when HAL, the ship's AI, calmly says, "I'm sorry, Dave, I'm afraid I can't do that."

It's chilling. Not because the AI is evil, but because it's confident, logical, and completely missing the human context.

Now imagine HAL isn't on a spaceship. It's watching over your factory network.

Still sound like fiction? It's not. AI agents are already at work in industrial environments. Some observe. Some act. And all of them need one thing: boundaries.

So, What Is an AI Agent?

An AI agent is a system that perceives its environment, processes what it senses, decides on an action, and then acts – often autonomously.

What Makes an AI Agent Unique?

A real agent has:

Perception: It takes in data from the world (logs, sensors, commands, images).
Decision-making: It interprets that input, plans next steps.
Action: It does something – responds, alerts, blocks, moves.
Learning: Over time, it can get better via machine learning.

Think of it like a digital co-worker that watches, reasons, acts – and learns from the result.

That’s the idea behind an AI agent.

It can:

Combine data from multiple sources (network traffic, device activity, user behavior)
Learn from past incidents ("This looked like an attack last time")
Suggest what to do next – or act automatically

The difference from traditional systems? It doesn’t follow rigid rules. It adapts.

What AI Agents Are Not

Before you hear: "We already have that", here's what AI agents often get confused with:

Chatbots: These are designed to answer questions, not to act on infrastructure.
Scripting & Rules Engines: These do the same thing every time. Agents adapt.
RPA (Robotic Process Automation): Great at clicking buttons. Not great at learning from evolving security threats.

If it doesn’t learn from patterns or adapt to your environment, it’s not an AI agent.

Think of agents as the difference between a blinking warning light and a seasoned shift supervisor. One signals, the other acts with judgment.

How Do AI Agents Actually Work?

Imagine you're watching dozens of security cameras. You can't focus on all of them at once, so you hire a helper.

This helper:

Watches everything nonstop
Remembers what "normal" looks like
Tells you if something's off

That helper is your AI agent.

But instead of eyes, it uses math. Specifically:

Data In: System records, alerts, communication logs, device identities
Pattern Recognition: It learns what's usual (and what's not)
Decision Layer: It flags things that look strange or risky based on its learned behavior

It learns over time – not like a human, but more like a spreadsheet nerd with endless memory. It doesn’t "understand" your factory. But it sees patterns humans often miss.

Think of it like a junior tech who’s fast, doesn’t sleep, but still needs supervision.

A Quick Example

A classic rule-based security system might ask:

"Is someone trying to access Port 445? Then trigger an alarm."

An AI agent, meanwhile, thinks more like this:

"This device is acting strangely. It's suddenly talking to a server we've never seen before. That's suspicious."

That’s powerful – because attackers today are often spotted by their behavior, not just their tools.

Types and Examples of AI Agents

Not all agents are the same. Here are common types:

Reactive Agents: Simple, fast responders. No memory. Think: a chess AI reacting to each move.
Planning Agents: They build and follow strategies.
Learning Agents: Improve with experience.
Multi-Agent Systems: Multiple agents collaborating or competing (e.g., swarm robotics or trading bots).

And where do they show up?

Virtual Assistants: Siri, Alexa – listening, interpreting, acting.
Autonomous Robots: Navigating warehouses, helping in care homes.
Smart Chatbots: Like ChatGPT when it schedules, composes, extracts.
Web Agents: Bots comparing prices, detecting fraud, curating news.

Where Else Are AI Agents Being Used?

This pattern isn't unique to cybersecurity. AI agents are showing up across industrial operations:

1. Predictive Maintenance

"This pump's vibration pattern is subtly shifting. It's not urgent yet, but based on similar patterns last quarter, it could fail within days."

2. Energy Optimization

"Delay this heating process by 30 minutes to avoid peak energy rates."

3. Quality Control

"These welds look fine to the naked eye, but the surface texture is off by 2%. Flag for inspection."

4. Inventory and Logistics

"Reorder this part now to avoid a two-day halt next Tuesday."

5. Support and Incident Handling

"This is the third temperature warning on Line 3 in one week. Recommend inspecting the ventilation unit."

Note: These use cases only work if agents are trained on good data and operate with clear boundaries. Otherwise, they become just another alert machine.

How Is This Different from Classic Machine Learning?

This question comes up a lot – and it's important to get it right:

A machine learning model finds patterns. An AI agent uses those patterns to decide and act.

Machine Learning (ML) is the brain. But it doesn’t move.

Learns from data: "Is this an attack?" Yes/no.
Returns a probability or prediction.
Doesn’t act on its own.

Example:

"This traffic looks 87% suspicious." → Now someone needs to decide what to do.

AI Agent = ML + Action Layer

Watches, learns, suggests and acts if allowed.
Uses playbooks (e.g., block device, raise ticket).
Interacts with systems, not just dashboards.

Example:

"Suspicious behavior. Blocking the port and sending a message to ops."

How Do You Know If It’s Working?

When AI is involved, it’s easy to get dazzled. So here are questions worth asking:

Does it only spot what it was trained on or also things it’s never seen?
Can you explain why it triggered an alert?
Is it getting better with time or just noisier?
Is there a human-friendly way to pause, override or re-train it?

If you can’t answer those questions clearly, your agent might be more black box than assistant.

What About SOAR?

SOAR stands for:
Security Orchestration, Automation, and Response.

It simply means:

Automatically analyze security incidents
Prep responses (e.g., block a port, send an email, disable a user)
Log everything for later review

Think of it as a digital emergency toolkit with a flexible playbook.

Many SOAR platforms also connect to helpdesk tools, status dashboards, and external threat intelligence feeds. That means they're not just reacting, they're part of a broader loop.

AI agents can actively use this kit. They don’t just detect problems, they also launch the appropriate response.

What Do You Actually Gain?

If done right, AI agents can take the pressure off your team and make decisions clearer:

Time saved: They handle the repetitive stuff first. Less triage, more action.
Clarity in chaos: They sort signal from noise when dozens of alerts compete.
Consistency: They react the same way every time, based on logic, not fatigue.
Faster response: Less waiting, less email ping-pong. Action starts while humans are notified.
Better decisions: By surfacing context, past cases, and patterns humans might miss.

But none of this comes "out of the box." You have to give the agent clear boundaries and coach it like a junior team member.

Who Do You Talk to Internally?

Before jumping in, align with these key roles:

IT Security / CISO: For security-related use cases. Ask: "Can we test an agent for unusual login detection?"
OT or Production Leads: For maintenance, quality, or process insights. Ask: "Where do repeated issues pop up that an agent could flag early?"
IT Infrastructure: For access to data and safe deployment. Ask: "How can we feed logs into a pilot setup without risking stability?"

If internal support is thin, consider involving an experienced system integrator, MSSP, or tech partner with industry focus.

So How Do You Actually Implement One?

It starts small. Like hiring a junior colleague:

Pick one use case
Connect the data sources
Set clear rules and limits
Define roles and reviews
Let it learn (but locally)
Test. Observe. Adjust.

Bottom line: Start with a clear “job description”. Give the agent the tools and guardrails it needs. And let it prove itself.

Where Things Can Go Wrong

An AI agent can trigger actions. But it doesn't know your plant's context.

Example: It blocks a device sending large volumes of data. Bad call - it was uploading recipe data mid-shift.

That’s why you need:

Guardrails
Clear rules
And, yes, plain old human common sense

Also important: Don’t just measure success by how many alerts were blocked. Ask:

How many relevant incidents were caught?
Were any real threats missed?

Quick glossary:
False positives = harmless things marked as threats
False negatives = real threats that went unnoticed

Five Questions to Ask Before You Deploy One

What is the agent allowed to do?
Is everything logged?
Who has the final say?
How does it learn?
Is OT in the loop?

Bonus: Who manages the policy rules and exceptions?

Security Risks You Shouldn't Ignore

Deploying AI agents introduces new potential attack surfaces:

Manipulated Inputs (Poisoning)
Over-automation and unintended actions
Blind Trust in Generic Models
Access Misuse or Credential Leaks
Cloud Dependency Risks

Bottom line: Always treat your agent like a powerful tool. Set rules. Monitor it. Question its decisions.

Final Thoughts

Remember one thing:

Use AI agents where speed and pattern recognition help. But never forget: context still lives with the humans.

AI agents aren't magic. But when they’re trained and tuned to your shopfloor, they can take pressure off your team, cut through noise, and keep things moving when every second counts.

That said: the smarter the agent, the more important your judgment becomes. Because in the end, it's not about replacing people – it's about helping them do their best work.

And if you’re leading a team: Treat AI agents like junior staff. Assign them, support them and don’t leave them unsupervised. After all, even HAL sounded helpful until he stopped listening.