Self-Patching with Misinformation: Why People “Update” Themselves with Bad Data

In today’s cyber landscape, everyone is doing their best to stay ahead. But what happens when your people go looking for answers and end up with the wrong ones?

We call it self-patching with misinformation.

In the absence of structured, contextual, and ongoing learning, employees often fill in the gaps themselves. They Google for answers. They ask ChatGPT. They watch TikTok explainers. They get cybersecurity tips from morning news shows or forwarded WhatsApp messages. They do what humans do best: adapt. But in a high-stakes environment where the speed and scale of threats evolve faster than most internal training programs, these self-initiated workarounds can backfire—badly.

What You'll Learn: People “self-patch” with bad data because it’s fast, novel, and identity-congruent.

• False content spreads faster and farther than true content—humans (not bots) are the main driver. MIT News

• Cognitive & motivational biases + algorithmic amplification make misinformation feel right. Frontiers

• Debunking helps, but prebunking/inoculation can be more effective upstream.

• Treat “self-patching” as a behavioral risk: redesign environments, prompts, and habits—not just policies.

What is the The Learning Cliff in Security Awareness?

Many human risk and awareness programs today leave employees dangling on the edge of what we call the learning cliff:

• One annual training on phishing, then silence.

• A static intranet page that hasn’t been updated since 2020.

• No clear wayfinding or information architecture for seeking help.

• Zero reinforcement mechanisms to encourage curiosity, growth, or mastery.

And it’s not because the program leaders don’t care. Quite the opposite. Most of the awareness and HRM leads we talk to want to build more comprehensive, human-centered experiences. They just don’t have the resources, time, or internal support to do so. That’s the real problem: when the system doesn't support continued enablement, people seek out information elsewhere.

Misinformation Loves a Vacuum

When internal systems go quiet, external noise fills the void.

People begin to rely on:

• YouTube creators who conflate social engineering with magic tricks.

• Marketing videos that oversimplify ransomware prevention.

• Personal TikToks that confuse compliance with security.

• Outdated blogs that don’t reflect the current threat landscape.

Worse, this fragmented advice becomes a new baseline for decision-making. If someone learns from an influencer that "hovering over links is enough," they might not be prepared for the new generation of QR code or image-based phishing attacks. If another person reads a viral blog that insists "Macs don’t get viruses," they’re less likely to report anomalies.

Let Curiosity Lead, But Don’t Let It Go Unsupervised

The goal isn’t to stop people from being curious or self-directed. In fact, that would be a mistake. The best employees are the ones who take initiative to learn. The trick is to give them a better option: a modern, branded, trustworthy, pull-based learning journey that grows with them.

This means:

• Replacing static content with dynamic, up-to-date experiences.

• Embedding wayfinding and searchability across your internal tools.

• Designing for competency clusters that reinforce knowledge across behaviors.

• Mapping learning experiences to risk, role, and context.

• Making the "why" behind the risk personal, relevant, and engaging.

We’ve seen the difference this makes: not only in reducing incidents, but in building a culture of cyber ownership.

You Don't Need to Boil the Ocean

If you’re a program lead reading this and thinking, "We just don’t have the budget," you’re not alone. But scaling doesn’t have to mean building Netflix for cybersecurity from scratch.

Start by:

• Identifying your biggest learning cliffs. Where are people falling off?

• Conducting a micro audit of your internal resources. Is your information architecture human-friendly?

• Asking employees where they actually go for cybersecurity advice.

• Introducing small feedback loops to check for misinformation.

• Curating or sourcing content that meets your standards but also matches employee habits.

Or, better yet—let us show you how we do it. Our autonomous learning journey is designed to meet people where they are, keep them informed with content they actually enjoy, and build capability without burning out your team.

Follow us on LinkedIn for more insights on cyber learning journeys, or get in touch with our team to see how we can help replace self-patching with structured, scalable enablement.

Key Takeaways: Reducing “bad-data” self-patching

• Assume the infodemic: volume, velocity, and emotion tilt toward falsehood; design controls accordingly.

• Shift left with prebunking: inoculate people with techniques of manipulation (e.g., scapegoating, fake experts) before exposure.

• Use evidence-based debunking: clear headline, fact first, concise myth, explain why wrong, repeat the fact. Skeptical Science

• Engineer the feed: defaults and frictions (mute/limit low-cred sources, “think-before-share” prompts) nudge better intake. ScienceDirect

• Measure behavior, not just awareness: track sharing habits, source diversity, and time-to-verification as human-risk signals.

Frequently Asked Questions About Misinformation “Self-Patching”

Why does misinformation spread faster than the truth?

Large-scale analyses on Twitter found falsehoods travel farther, faster, deeper than true stories—driven mainly by humans sharing novel, emotional content.

Aren’t bots the main problem?

Bots matter, but even when bots are removed from analyses, humans still propel false news more than truth—novelty and emotion drive sharing. 

What actually works to counter bad updates—debunking or prebunking?

Use both: debunk with evidence-based formats (fact → myth → fallacy → fact), and prebunk by teaching common manipulation tactics in advance (inoculation). 

Why treat misinformation as a security and culture problem?

Infodemics show that misinformation degrades decision-quality at scale; biases + algorithms amplify it. Treat intake behaviors as a human-risk vector and build resilience like any other control.