Notifications
Clear all
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.
NHIMG editorial — based on content published by Safetica: AI hype in cybersecurity vendor offerings
By the numbers:
- Over 61% of American adults used AI in the first six months of 2025, and nearly one in five rely on it daily.
- Only 22% of SOC analysts agree that AI boosts productivity, even as 71% of executives claim it does.
Questions worth separating out
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: When does AI-assisted security tooling create more risk than it reduces?
A: Risk rises when the system can influence decisions without clear entitlement boundaries, traceability, or human review.
Q: What do security teams get wrong about AI features inside cloud security platforms?
A: They often assume AI features are only about better analytics, when the bigger issue is whether those features influence access, response, or automation decisions.
Practitioner guidance
- Test the mechanism, not the label Ask vendors to show exactly what the AI feature does, what data it uses, what model or logic drives the output, and where human review still applies.
- Measure operational outcomes before wider rollout Compare false positive rates, triage time, and containment quality with and without the AI feature so you can see whether it improves security or only changes the interface.
- Constrain automation with explicit approval paths Limit automated lockout, blocking, or containment actions to high-confidence conditions and require documented escalation thresholds for lower-confidence events.
What's in the full article
Safetica's full article covers the operational detail this post intentionally leaves for the source:
- Specific examples of how vendors rebrand legacy features as AI and how to challenge those claims in procurement
- The practical differences between useful automation and noisy automation in SOC and security workflows
- Examples of when AI-assisted lockout or filtering helps versus when it creates avoidable disruption
- The vendor's own guidance on balancing outcomes, evidence, and productivity claims
👉 Read Safetica's analysis of AI hype in cybersecurity vendor offerings →
AI security features in cybersecurity: are your controls keeping up?
Explore further
18/08/2026 3:20 pm
AI security procurement is now a governance problem, not a feature-selection exercise. The article shows how quickly AI labels can outpace verification, especially when older capabilities are repackaged as new controls. For IAM, PAM, and NHI programmes, that same pattern appears when teams buy trust in a label instead of testing the lifecycle, privilege, and audit consequences of the feature. Practitioners should evaluate outcomes, not branding.
A question worth separating out:
Q: How do organisations keep AI features from weakening privacy and access control?
A: Treat prompts, transcripts, outputs, and admin consoles as governed data and identity paths. Apply access reviews, retention rules, logging, and data-classification controls to the AI workflow, not just the surrounding platform. That keeps sensitive information from leaking through convenience features or unmanaged administrative access.
👉 Read our full editorial: AI hype in cybersecurity is exposing weak procurement discipline
