For years, the tech industry has told the story of Paul Bunyan, the giant lumberjack who lost to a steam-powered saw because he relied only on his strength and ignored new tools. Today, in cybersecurity, you're facing a similar situation. AI is that “steam saw” already reshaping the way we work—whether we like it or not. The difference? You can't afford to ignore it.
AI is already embedded in your platforms, your workflows, and every tool you use. The real question is: are you leveraging it—or is it controlling you?
At TecnetOne, we’ve spent months observing how security teams that smartly integrate AI move faster, reduce workload, and improve the quality of their investigations. But we’ve also seen the opposite: organizations that blindly trust automated tools without understanding how they make decisions. That mistake can be costly.
This article walks you through how to make AI a strategic advantage—not a threat—and why you need to master it before it outpaces you.
You may not realize it, but nearly all your security tools already use AI models:
The problem? Most of those “smart” decisions happen behind the scenes. You don’t know what model is used, what data trained it, or what biases it holds. And when the tool makes a mistake, the accountability is still yours.
These models don’t know your business, your risk priorities, or your workflow. They just apply statistical logic. That means a tool can label a real threat as “low risk”—or flag noise as a high priority.
That’s why you need something more: AI supervised by you, with your rules and your business context.
This isn’t about reinventing the wheel. You don’t need to build tools from scratch. Your job is to create custom workflows or microtools where you define:
Doing this gives you a massive edge: you can patch the blind spots of commercial platforms and enrich your operations with models tailored to your needs.
And here’s the best part: you don’t need to be a machine learning expert anymore. Today, you can describe what you want in natural language, and the AI will write most of the code. Your job is to refine, test, and align it with your actual risks.
Read more: Why should you NOT ask an AI to create your passwords?
A large part of a security analyst’s time isn’t spent investigating—it’s spent translating:
Each task breaks your flow, slows you down, and delays investigations, incident response, or forensics.
AI can take over this translation layer.
Imagine saying:
"Bring me all events tied to this host from the last 12 hours and filter only those with unusual PowerShell activity."
And getting:
No syntax, no wasted time.
When AI handles the repetition, you focus on making sense of the data and driving decisions.
This is where many teams go wrong.
AI can process more data than any human. But that doesn’t mean it can replace your judgment.
AI reasons in statistics, not ethics. It mimics smart advice—it doesn’t live with the consequences.
That’s why your role matters more than ever. Whether you’re in offensive, defensive, or forensic cybersecurity, you decide the actions. AI is your accelerator—not your autopilot.
You might also be interested in: AI Agents: Only as Smart as the Database Behind Them
To truly harness this new era of AI, you need three foundational skills. They’re not complex—but they’re critical.
AI will write most of the code, but you must be able to:
Basic Python knowledge is enough to stay in control.
This helps you spot:
This is key to catching dangerous output.
You don’t need to train models—but you should know:
This lets you make informed decisions and keep ownership.
Here’s a simple roadmap we use at TecnetOne with teams new to AI:
These wins stack up fast. In three months, you could have a custom AI ecosystem that lifts a huge load off your team.
AI doesn’t eliminate the need for analysts.
It raises the bar.
Now you need judgment, strategy, and the ability to drive powerful tools.
If you learn how to wield AI, it becomes your competitive edge.
If you ignore it, you risk becoming Paul Bunyan—strong, but outpaced by a tool you refused to use.