There’s a lot of noise right now about AI taking over jobs. Some of it’s hype, some of it’s fear, and a little of it’s reality. I get it — I’ve seen the same reactions inside cybersecurity teams, engineering orgs, and risk functions. When automation or AI tools start showing up in workflows, people naturally wonder what that means for their future.
Here’s the truth: yes, some jobs will change or even disappear. But there’s also a massive opportunity for the folks who learn how to work with AI — not against it. The future isn’t about humans being replaced; it’s about humans being augmented. The people who understand that early will have the advantage.
That’s what I mean when I say AI durable. It’s not about surviving AI — it’s about staying relevant because you’ve adapted, stayed curious, and found where the human still matters most.
Step 1: Start With Mindset
The hardest part of change is usually psychological.
When I first saw automation rolling into security operations, there was panic — folks thought scripts, SOAR playbooks, and detection-as-code meant fewer analysts. What actually happened was that the analysts who leaned in, learned to fine-tune playbooks, and figured out where automation failed became the go-to experts.
That’s the play: learn to steer the system, not fight it.
AI will automate the repetitive parts of work — not the judgment, not the decision-making, and not the creative problem solving. If you focus your energy on those parts, you’ll always have a role to play.
Step 2: Look Around and Spot AI Opportunities
Before worrying about what AI might take away, look for what it can improve.
Every role has patterns — reports you write every week, data you clean up, emails you draft, metrics you repeat. Those are all candidates for AI assistance.
A few questions to ask yourself:
- What eats up the most time but doesn’t really require judgment?
- Which tasks could I accelerate with a model, script, or automation?
- What still needs a human check or a final decision?
In cybersecurity, for example, AI can summarize threat intel or flag anomalies faster than we can, but a human still needs to validate what’s real and what’s noise. In GRC, AI can pull control evidence, but it can’t tell you if the control actually works.
That’s your lane — stay in the loop, guide the tool, and build confidence in its output.
Step 3: Participate in the Change
If you wait for your organization to hand you an “AI plan,” you’ll be behind.
Start experimenting now. Use the tools that are already approved — ChatGPT, Copilot, or whatever your company’s cleared. Try small wins:
- Automate documentation or test cases.
- Use AI to brainstorm detection logic.
- Draft but then review — so you stay the editor, not the consumer.
When leaders start asking, “Who’s actually using this stuff effectively?”, you’ll have real examples. That’s how you move from anxious to indispensable.
Step 4: Build Skills That Last
To be durable in an AI world, focus your upskilling around three areas:
1. Understanding AI (Without Becoming a Data Scientist)
You don’t need to build a model from scratch, but you should understand how one works. Learn the basics: how models are trained, what “bias” actually means, and why context matters.
2. Data Awareness
AI lives on data — how clean it is, how it’s structured, and how it flows. Get comfortable with simple data tools, APIs, and structured formats like JSON or CSV. Even a light familiarity gives you an edge.
3. Critical Oversight
This is the secret skill. Learn to review, challenge, and improve AI output. Know when to trust it and when to question it. Those who can validate and explain AI’s decisions will be the ones in demand.
There are plenty of free or low-cost ways to learn this — Coursera, MIT OpenCourseWare, YouTube crash courses, or even open datasets to practice on. The main thing is consistency. You don’t need to master it all — just make progress every week.
Step 5: Be the Human in the Loop
Every AI system still needs human oversight — someone who can say, “That doesn’t look right,” or “That’s not ethical,” or “This isn’t what we intended.”
That’s your north star. The most resilient professionals will be the ones who bridge the technical and the practical. You don’t have to be the person writing Python code — being the one who can translate what the code does and why it matters is just as valuable.
Think of it like air-traffic control: you’re not flying the planes, but you’re keeping them from crashing.
Step 6: Keep a Positive Grip on the Future
AI isn’t the enemy — stagnation is.
The people who succeed in this next chapter aren’t necessarily the most technical. They’re the most curious. They’re the ones who see change as an invitation to learn, not a threat to stability.
If you can keep that mindset — exploring new tools, experimenting with new workflows, sharing what you learn — you’ll not only stay employable, you’ll be leading others through the transition.
So yeah, the world’s changing fast. But that’s always been true. The question is whether you want to watch it happen or help shape it.
Becoming AI durable starts with one small step: lean in.
