Security outcomes vs. AI optics: what’s really improving?

One of the first realities CISOs must accept is that AI in cyber security isn’t new. Machine learning (ML) has powered spam filters, anomaly detection, user behaviour analysis, and fraud systems for over a decade. What is new is the arrival of large language models (LLMs) and more accessible AI tooling that vendors are rapidly layering onto existing products. This shift has changed how security teams interact with data – summaries instead of raw logs, conversational interfaces instead of query languages, and automated recommendations instead of static dashboards.

That can be genuinely helpful. But it also creates an illusion of intelligence, even though the underlying security fundamentals may not have changed. The mistake many organisations make is assuming that more AI automatically equals better security. It doesn’t.

One lesson that keeps resurfacing is that architecture beats features. An AI bolted onto a weak security foundation won’t save you. If identity is broken, data governance is unclear, or network visibility is fragmented, AI simply operates on bad inputs and produces unreliable outputs. CISOs also must understand that AI doesn’t replace fundamentals, it amplifies them.

AI washing: where security claims drift into hype

The issue of AI washing must be taken seriously, as vendors overstate or misrepresent the use of AI in products to capitalise on market hype rather than deliver real capability. In cyber security, this often means rebranding traditional rules, heuristics, or basic automation as “AI-powered” without meaningful innovation or measurable outcomes. AI washing confuses buyers, inflates expectations and obscures real risks by hiding behind vague claims and opaque models.

Problems arise when AI is positioned as fully autonomous, self-healing or capable of replacing human judgement altogether. In practice, these claims often obscure significant limitations. One red flag in vendor pitching is AI opacity. If vendors cannot clearly explain what data the AI uses, how decisions are made or how errors are handled, CISOs should be cautious. Recognising these limitations helps security leaders feel prepared and avoid overreliance on unproven claims. 

For CISOs, the danger is not just wasted investment, but adopting tools that add complexity without improving security posture.

AI is a force and value multiplier

AI is a force and value multiplier, not because it replaces people or processes, but because it amplifies what already exists. In cyber security, AI accelerates detection, scales analysis and helps teams make faster, more informed decisions across massive volumes of data that humans alone cannot handle. When paired with strong architecture, quality telemetry and clear operational intent, AI increases efficiency, reach and impact. The real value of AI lies not in automation alone, but in how effectively organisations design, govern and operationalise it. There are several areas where AI-driven security capabilities are already delivering tangible benefits.

Threat detection at scale remains one of AI’s strongest use cases. Modern environments generate more telemetry than humans can realistically analyse. AI excels at spotting patterns across network flows, identity behaviour, endpoint activity and cloud signals – especially when attackers deliberately blend into everyday operations.

There are also clear benefits in security operations and triage. LLMs can summarise incidents, explain why an alert matters, correlate signals across tools and reduce investigation time. This doesn’t replace analysts, but it significantly improves productivity, providing an essential advantage in an era of staffing shortages.

The third area where AI can make a significant difference is in detection engineering and gap analysis. It can help teams reason about coverage, suggest new detections and identify blind spots in policy enforcement. When used carefully, it strengthens defensive posture without increasing noise.

In these cases, AI acts as a force multiplier, not a decision-maker, and that distinction matters.

The questions CISOs should be asking

To cut through the noise, CISOs should shift vendor conversations away from AI buzzwords and toward operational reality. The aim should be to discuss the contextual use of AI in cyber security. Asking targeted questions, such as what specific security problems AI addresses better than existing tools or how errors are managed, can help evaluate real capabilities and avoid hype.

• What specific security problem does this AI solve better than existing tools?
• What happens when the AI is wrong – and how often does that happen?
• Is human oversight built into the workflow or optional?
• What data leaves our environment and how is it protected?
• How does this integrate with our current architecture and controls?

The goal isn’t to avoid AI, it’s to ensure AI strengthens security rather than introducing new, unmanaged risk. By adopting AI thoughtfully and with a clear understanding, CISOs can feel empowered to make strategic decisions that enhance security without unnecessarily exposing themselves to unknowns.

Making the right decision for your organisation

The right AI security investment depends on maturity. For some organisations, the biggest win is AI-assisted visibility and triage. For others, it’s detection engineering or behavioural analytics. Very few are ready for a complete autonomous response – and that’s okay. CISOs who succeed with AI take a measured, use-case–driven approach. They pilot, validate outcomes and retain human accountability. They demand clarity, not buzzwords. And they remember that security is ultimately about risk reduction, not technological novelty.

AI is neither a silver bullet nor a fad in cyber security. It’s a powerful tool – one that can meaningfully improve defence when applied thoughtfully, and just as easily create new risks when adopted unquestioningly. For CISOs and buyers, the goal isn’t to buy “AI security”. It’s to buy security that uses AI responsibly, transparently and effectively. The organisations that get this right won’t be the ones with the most AI; they’ll be the ones that made the most intelligent choices about where, why and how to use it.

Aditya K Sood is vice president of security engineering and AI strategy at Aryaka.