Topic starter
18/08/2026 2:34 pm
TL;DR: AI adoption is growing fast, but much of the cybersecurity market is repackaging old capabilities as AI while adding alert noise, privacy risk, and procurement confusion, according to Safetica; buyers should demand proof of outcome, not marketing language. The practical issue is that hype can expand attack surface and weaken operational discipline if teams do not test how these features behave in real workflows.
Editorial analysis by NHI Mgmt Group, based on content published by Safetica: “What to Look for in a Vendor’s AI Features”.
By the numbers:
- By 2025, global AI adoption will reach 378 million users, according to Safetica.
- Over 61% of American adults used AI in the first six months of 2025, according to Safetica.
Key questions
Q: How should security teams evaluate AI claims in cybersecurity tools?
A: They should evaluate the tool by its actual decision behaviour, not by marketing language.
Q: What breaks when AI features are just repackaged legacy controls?
A: Procurement and governance break first, because teams buy a promise of new capability while receiving the same control in a new wrapper.
Q: Why do AI security tools often create more alert noise?
A: They often inherit the same tuning problems as older analytics tools, then add a layer of opacity that makes calibration harder.
Practitioner guidance
- Challenge the mechanism behind the AI label Ask vendors to explain exactly what the feature does, whether it is classification, automation, decision support, or true agentic execution, and require a demonstration in your own workflow.
- Measure security outcome before approving spend Compare claimed AI features against baseline outcomes such as false positives, alert volume, investigation time, and containment speed before you commit to rollout.
- Test privacy and retention boundaries explicitly Review what data the feature consumes, stores, transcribes, or retrains on, especially where user content, endpoint activity, or meeting data may be processed.
Bottom line: AI-labeled security features can conceal familiar mechanisms, which means procurement teams need proof of control improvement rather than promises of novelty.
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18/08/2026 3:20 pm
AI procurement has become a control problem, not a feature-shopping exercise. The core weakness exposed by this article is the assumption that an AI label signals a security gain. In practice, buyers are often comparing marketing language instead of measurable outcomes such as fewer false positives, lower analyst workload, or safer data handling. The practitioner conclusion is simple: if the security effect cannot be demonstrated, the claim should not influence architecture or budget.
A few things that frame the scale:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern AI systems that can make consequential decisions?
A: Organisations should govern consequential AI systems with the same discipline used for high-risk identities: defined ownership, least privilege, logging, approval boundaries, and human override. The critical requirement is to connect model behaviour to real access paths so legal review, security review, and audit evidence all describe the same system.
👉 Read our full editorial: AI hype in cybersecurity is exposing weak procurement discipline