AI in Cybersecurity: Why It’s Not a Good Idea… Right Now

Artificial intelligence (AI) is rapidly making its way into every industry. From self-driving cars to personalized content recommendations, AI has proven to be a powerful, transformative force. So it’s no surprise that cybersecurity professionals are exploring how to leverage AI to protect digital assets.

But here’s the thing: while AI might seem like the perfect solution to combat modern cyber threats, in reality, relying too heavily on it right now could do more harm than good.

Here’s why.

1. The Hype is Outpacing Reality

If you’ve spent any time around cybersecurity product demos or tech expos, you’ve probably heard phrases like “autonomous threat detection,” “self-healing systems,” or “zero-touch response.” These sound revolutionary, but the truth is, most of these capabilities are still in their infancy.

AI-powered tools often rely on carefully curated scenarios. Outside of those conditions, they struggle. This gap between marketing and real-world functionality can lead to overconfidence in tools that aren’t ready for prime time.

2. AI Doesn’t Understand Context

Human analysts excel at understanding nuance—whether it’s recognizing the subtle signs of a phishing attempt or interpreting strange network behavior in context. AI, on the other hand, operates on patterns and probabilities. Without deep domain understanding, it’s prone to:

False positives: Flagging legitimate user behavior as malicious.
False negatives: Missing new or novel attacks that don’t fit learned patterns.

Cybersecurity requires precision. A flood of false alerts can cause alert fatigue, while missed threats can lead to breaches.

3. Adversaries Are Evolving Too

It’s not just defenders who are using AI—attackers are getting smarter too. We’re now seeing:

Adversarial attacks, where malicious actors manipulate input data to confuse AI models.
AI-crafted malware, designed specifically to evade detection systems.
Deepfake phishing, using AI to clone voices and faces to bypass traditional verification.

Introducing AI into cybersecurity without robust safeguards can unintentionally open new attack vectors.

4. AI Needs Data—Lots of It

Training an effective AI system requires massive amounts of high-quality, relevant data. But in cybersecurity:

Threat landscapes change quickly.
Data is often proprietary, siloed, or limited.
Labeling that data accurately requires deep expertise.

Without a solid, well-maintained dataset, AI models risk becoming outdated or dangerously biased.

5. Overreliance Can Lead to Complacency

AI is meant to augment, not replace human judgment. But there’s a growing risk of teams putting too much trust in automated tools. This can lead to:

Diminished analyst skills over time.
Blind spots where AI coverage is incomplete.
Critical decisions being made without human oversight.

Security is a discipline that demands vigilance and adaptability—qualities AI doesn’t yet possess.

6. Ethical and Regulatory Concerns

AI in security raises big questions:

If an AI system fails to detect a breach, who’s responsible?
How transparent are the decisions made by AI?
Can automated defenses take actions that harm users or violate privacy laws?

With data privacy regulations tightening globally, deploying AI without clear accountability can expose organizations to legal and ethical trouble.

Use AI Wisely—But Don’t Hand Over the Keys Just Yet

AI is an incredible tool with enormous potential in cybersecurity. It can analyze large volumes of data, identify patterns, and accelerate response times. But it’s not a magic wand—and it’s definitely not a replacement for experienced human professionals.

Right now, Kinetic utilizes a hybrid model: use AI to assist, not to lead. Combine its speed and scale with human intuition, ethics, and judgment.

Until AI matures further—and until we can trust it with more than just surface-level decision-making—it’s wise to keep the humans in the loop.