RESOLVING THE LEDGER
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Many organizations believe they are successfully leveraging AI tools to strengthen their security posture.
Investment is rising, AI is being embedded across multiple workloads and workflows, and governance frameworks continue to expand, giving the impression on the surface that progress is being made.
But beneath this AI adoption lies an unseen problem: a growing gap between what organizations believe about their infrastructure security, and what they can actually evidence.
Confidence is rising, but so are breaches
According to a global 2026 Hybrid Cloud Security Survey, which gathered the views of more than 1,000 Security and IT leaders, 93 percent have invested in new security technologies, yet despite this, breach rates have hit their highest point. Sixty-five per cent of organizations experienced a data breach in the past 12 months, an 18 percent rise year on year, and a near 40 percent rise over three years.
These worrying statistics are starting to ring alarm bells, highlighting that within organizations we are starting to see an ‘illusion of security’ creeping in. Organizations are investing heavily in security tools, yet still seem to lack clear visibility into the outcomes of their investments. In other words, leaving security to be measured by what has been implemented, rather than what can actually be verified.
A perfect storm of conditions has brought us to this point: AI adoption has scaled faster than governance and security teams can keep pace with, and hybrid cloud has added another dimension of complexity that few organizations have fully reckoned with.
AI is now embedded across enterprise environments, accelerating not just how organizations operate, but how risk moves through them. As adoption has outpaced oversight, the consequences are starting to surface, with nearly half of organizations surveyed reporting a rise in AI-related insider threats, including data leaks, and unsanctioned use (shadow AI). Perhaps surprisingly confidence hasn't reduced. Many organizations continue to classify their AI security posture as "defined" or "integrated," despite evidence portraying a radically different story.
That confidence often rests on assumptions, and the scale of the disconnect is striking; AI is now involved in 83 percent of security incidents, spanning external attacks, internal exposures, and direct targeting of AI systems. As threats move faster across increasingly fragmented and distributed environments, assumptions about what’s secure quickly fall apart and without clear visibility into how data moves and systems behave, organizations cannot reliably manage risk.
The warning signs
No single indicator reveals a false sense of AI security, but several recurring patterns make it obvious.
The first is investment without impact. Many organizations are expanding their security stacks, yet detection and response times are still moving in the wrong direction. More than 40 percent report that it now takes longer to detect and investigate breaches than it did previously, and that's not a coincidence.
When signals are spread across systems that don't connect, teams spend longer piecing together what happened rather than acting on it. Adding tools without improving visibility doesn't create more clarity. It creates more data, and more data without context is just more noise to wade through not to mention more false positives and perhaps even worse more false negatives.
The second is an inability to trace incidents back to their source. More than one in four organizations cannot determine the root cause of a breach, which means incidents are being closed out without fully understanding how they happened. The consequences are predictable: nearly one-third of organizations report multiple incidents within the same year. Without traceability, there is no learning, and without learning, the same gaps simply get exploited again.
The third is the visibility gap opening in AI-driven environments. With nearly three-quarters of organizations reporting limited visibility into AI-driven data flows, which span APIs, models, cloud services , and distributed infrastructure that is dynamic by design and doesn't map cleanly onto traditional monitoring approaches. Security teams can often see that something happened, but simply can’t get to the how or the why. That gap between knowing an incident occurred and understanding it, is exactly where the illusion lives.
Moving from illusion to evidence
In response to growing complexity, organizations are collecting more telemetry than ever; metrics, events, logs and traces (MELT data), but more data does not necessarily equate to better understanding. These signals each offer only a partial view: one measures performance, another records activity, other flags issues after the fact.
None of them, on their own, are able to explain how systems behave as a whole. What’s missing is the connective tissue, the context that shows how these signals relate, how one event triggers another, and how issues propagate across the environment. Without that, organizations aren’t gaining insight; they’re just accumulating noise.
Shattering the AI ‘security illusion’ requires a shift from reactive security to proactive monitoring and real-time observation of how systems behave. Security leaders agree, with more than 90 percent of organizations reporting that complete visibility across data in motion is critical to their successful security outcomes.
This is where network-derived telemetry becomes essential. Unlike logs, which show what systems say they’re doing, network telemetry proves what is happening: how data moves, how systems interact, and how threats develop. It's the shift from assumption to evidence and then proof, and it's the only foundation solid enough to build real security on. The network is the source of truth for today’s security teams.
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