Cybersecurity has long been sold as a fortress. We hear phrases like “military-grade encryption” and “ironclad infrastructure.” Yet the same story repeats: someone clicks a malicious link, leaves a port open, or reuses an old password.
The most sophisticated attacker rarely defeats the most sophisticated system. They defeat the least careful person connected to it.
In other words, the flaw isn’t only in code, it’s in conduct. Breaches don’t usually involve genius hackers outsmarting technology. They exploit trust, routine, and human error. Until we design systems with fallibility as the baseline, we’ll keep losing the same way.
You can patch code, but you can’t patch human nature.
People Are the Real Attack Surface
You can encrypt everything, isolate networks, and audit every line of code. But you can’t stop someone from clicking an email that looks like it came from their boss or ignoring a security prompt out of habit.
We’ve built infrastructures to keep outsiders out, but the easiest way in is through the front door wearing a trusted face.
Phishing, credential stuffing, and social engineering work because they prey on instinct: curiosity, panic, and urgency. The Slack token attack at EA happened when hackers simply asked an employee for access. The Twitch data leak involved misconfigured permissions. None were exotic zero-day exploits. They were trust exploits.
It’s reflex. Security tools can’t override that moment when your gut reaction takes over.
My solution: make the secure action the easiest one. Design systems that support, not frustrate, users. Phishing simulations shouldn’t be about blame. They’re a way to study behavior and build better defaults.
Security that annoys people gets bypassed. Design for real workflows under real pressure.
People will click. The question is: what happens next?
When the Call Is Coming from Inside the House
Many breaches begin with insiders taking shortcuts like unsecured tools, rushed setups, or skipped code reviews due to tight deadlines. These incidents usually stem from pressure, not sabotage.
In complex environments with cloud services and third-party APIs, risks build quietly and no one sees the full picture.
My approach, “intentional security,” focuses on creating a culture where everyone feels responsible. Developers don’t need to be security experts, but should have ownership and tools like secure defaults, embedded scanners, and safe ways to report risks.
The worst cases happen when someone notices a problem but stays silent. Rules alone do not catch mistakes. People do if the environment encourages speaking up.
Error Chains: Why Mistakes Happen
No breach begins with a single catastrophic act. It’s a chain of ordinary oversights: a missed update, a stale account, a misconfiguration. Under stress, these dominoes line up until one last nudge topples everything.
It’s never one thing. It’s a dozen little things happening in the wrong sequence.
I cite real examples:
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Capital One’s breach started with a misconfigured firewall.
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Uber’s leak came from hardcoded credentials in GitHub.
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Facebook’s massive data leak involved an abused API.
Good people in bad conditions will make bad choices. Not out of carelessness, but necessity.
The lesson: strong policies are only as good as the environment they live in. Instead of punishing error, I build systems that expect it: guardrails to limit the impact, automated checks, and post-incident reviews focused on learning rather than blame.
Every breach is a lesson plan. If you treat it as an embarrassment, you’ll learn nothing.
Can Automation Save Us?
If human error is inevitable, can automation fix it? To a point.
Machines don’t get tired. They don’t skip steps because they’re late to a meeting.
Automation excels at repetitive tasks: scanning code, enforcing configurations, and blocking outdated libraries. But it also mirrors the assumptions of whoever built it. If those assumptions are wrong, automation doesn’t just replicate mistakes, it scales them.
Bad automation is worse than none. It creates the illusion of safety.
The goal isn’t to replace human judgment but to amplify it. Automation should clear the noise so people can focus on nuance. But someone still has to ask: Does this make sense?
Cybersecurity is a human problem. Tools should support people, not sideline them.
The Simulation Approach
The best teams don’t wait for attackers to test their defenses. They run their own attacks: red teaming, phishing simulations, chaos drills.
You don’t wait for a fire to check if the exits work. You run the drill.
These exercises reveal gaps: an alert routed to the wrong Slack channel, an escalation policy hinging on someone who’s on vacation. The point isn’t to embarrass people. It’s to build muscle memory and data on how the organization responds under pressure.
Simulations won’t eliminate error. But they ensure you meet it on your terms, not the attacker’s.
The Inevitable Truth
Human error is not the exception; it’s the norm. You can’t eliminate it with policies, only design for it. The goal is not perfection, but resilience. Fast recovery comes from margin, preparation, and learning. Every missed red flag is a lesson. Blame won’t stop breaches, but psychological safety might.
