COMMENTARY: AI has become the cybersecurity industry’s shortcut to sounding modern. It shows up in just about all product names, pitch decks, and analyst briefings.There’s nothing wrong with using AI where it adds measurable value. Teams can benefit from systems that sift through telemetry at machine speed, correlate weak signals, or help analysts make sense of massive event volumes. The problem starts when AI becomes a marketing wrapper for every enterprise security function, whether or not it improves outcomes.[SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]Today, corporate leaders want to believe they can solve complex security gaps with autonomous tools. A platform that promises to detect, prioritize, and neutralize threats with minimal human intervention sounds like relief, especially to organizations short on talent and buried under alerts. But security isn’t always that convenient. In many cases, the AI claim describes a narrow feature, a basic anomaly score, or a rebranded rules engine dressed up in more ambitious language. Decision-makers may then assume the tool requires less tuning, less validation, and less operational discipline than it actually does.Overconfidence can weaken the habits that keep enterprises resilient: reviewing logs, testing controls, patching aggressively, and asking whether detection logic still matches the business environment. Some products are smart, but not always accountable. However, the buck stops with the management and security team.Hype can crowd out security basicsMost breaches do not begin with an attacker defeating a futuristic defensive model. They begin with exposed services, stolen credentials, unpatched systems, misconfigured cloud resources, poorly segmented networks, or employees tricked by a convincing message.These are not dazzling problems, but they keep showing up in incident reports. When AI-themed spending pulls attention away from them, we’re not looking at modernization, it’s more of an imbalance.Asset management offers a good example. Knowing what hardware, software, identities, cloud workloads, and third-party connections exist across the enterprise represents one of the least exciting parts of security. It’s also one of the most important. An organization cannot defend an asset it doesn’t know it owns.The same holds for patch governance, privileged access management, backup testing, phishing resistance, incident response planning, and the human side of defense. Security awareness training can benefit from bite-sized learning, but only when it reinforces practical behavior rather than becoming another check-the-box exercise.A practical issue emerges inside the security operations center. Analysts need evidence they can act on. If a system blocks a connection because a rule matched a malicious IP address, the workflow remains fairly clear. If a model flags a database query because it deviates from an opaque behavioral baseline, the team may have a harder time deciding what to do next.Probabilistic detection can surface patterns that static rules miss. However, when tools can’t explain their reasoning in operational terms, they can generate uncertainty instead of clarity.It’s a familiar outcome: more alerts, more triage, and more frustration. False positives consume analyst time, delay investigations, and make teams less responsive when a real intrusion unfolds slowly. If an AI-branded system increases ambiguous findings without improving prioritization, it has simply moved risk into the SOC queue.Budgets and skills do not scale with buzzwordsCybersecurity budgets are finite, and technical teams have limited attention spans. Every dollar spent on an inflated promise means dollars are not spent on penetration testing, identity hardening, secure development, tabletop exercises, or experienced analysts who understand the organization’s actual risk profile. The same discipline should apply to application security tools: they should reduce exploitable risk, not merely sound current in a board slide.Real machine learning systems require care. Models need quality data, relevant baselines, tuning, and monitoring as the enterprise changes. Cloud migrations, mergers, new business applications, and shifts in employee behavior can all alter what “normal” looks like. Without the right expertise, a sophisticated tool can become shelfware or a source of misleading assurance.Mid-sized organizations may not have data scientists, detection engineers, or mature security operations processes in place. Buying a product that assumes those capabilities does not magically create them.AI belongs in cybersecurity, but it should earn its place. Enterprises must ask vendors where machine learning gets used, what data trains the model, how conclusions are explained, how false positives are measured, and what operational work the customer must still perform.We should not ask whether a tool merely uses AI: it’s whether the tool measurably improves detection, response, prioritization, or resilience in the specific environment where it will operate.Cybersecurity does not need more vague intelligence claims. It needs transparency, disciplined execution, and controls that map to real risks. Organizations that keep that distinction clear will stay better protected than those that mistake branding for defense.David Balaban, owner, Privacy-PC SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Each contribution has a goal of bringing a unique voice to important cybersecurity topics. Content strives to be of the highest quality, objective and non-commercial.
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