Beyond AI-driven cyber security: why context and visibility are still the top priority of a CISO
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AI has been a real game check for productivity and automationBut as technology evolves, the expectations that are set out grow fast – especially in the field of cyber security.
From boarding rooms to soc teams, the promise is compelling: AI will reduce false positives, speed up detection, automate the response and not -efficient analysts. But although these options are certainly not out of reach, AI is not the “plug-and-play” solution that some may hope. Without the correct data, context and supervision, the lens offered by AI is at its best blurred.
According to a report of 2024 from IBM, about two -thirds of the organizations say they are now using AI Tools And automation in their SOC environments. A Darktrace survey from 2025, however, shows that less than half (42%) of Ciso’s trust in their AI deployment and fully understand how AI fits into their safety stack. This gap between AI implementation and the understanding of extracting value from this is not sustainable in the long term.
Exasters of interconnectivity
Networks used to be small, enclosed and relatively easy to protect – often limited to an office or single Cloud Computing environment. Nowadays they are vast webbes of interconnectivity that include multiple clouds and end point devices. In other words, cyber security has become more complex.
There is a growing assumption that AI can shed light on this complexity – that if you throw enough data to a model, it will separate the signal from the noise, even without deep integration in your area. But threats do not exist in a vacuum. They move through systems, exploit blind spots and adapt to patterns. And unless an AI system understands the operational basic line-what is normal, which is punished, which is really abnormal it is probably just the best. Sometimes it gambles well, but if this is not the case, the consequences can be expensive.
All this does not mean that AI is not forever strength. It is an incredibly powerful tool when it is used in the right way, but companies have to make themselves pace and create the right environment before it can really realize its promises.
Are companies prepared for AI?
The excitement around AI is not new or exclusive for cyber security. According to Gartner’s most recent hype cycle, both generative AI and cloud-based AI services are currently in the “Peak of Toplated Expectations” phase. What comes next, with every new technology, is the “trough of disillusion” – this is where the hype meets reality and industries realize that some lessons must be learned before the technology can rise to the last part of the cycle, “the plateau of productivity”.
This is precisely the pattern that security teams are now at AI. Early implementations have revealed how Bros Ai can be when they are removed from the controlled conditions of laboratory tests. Advanced models that look flawless in demos can falter in the complex, unpredictable context of a live tenter prize environment.
False positives are a problem. Analysts know the fatigue of chasing warnings that do not lead to anything – and AI, when incorrectly applied, can reinforce that sound instead of reducing it. But the greater risk is what AI misses. Algorithms trained on general threat data can fully overlook subtle, organizational deviations, such as a lateral movement that uses Piggybacks on a rarely internal tool, or data exfiltration by a legitimate integration of third parties. These are the species of threats that slips when detection efforts miss a specific environmental context.
Another reason for hesitation is that many AI-driven solutions work such as black boxes that against the grain of the SourceCommunity -driven threat reaction that industry is now right to. Their logic is not exposed, their training data is not transparent and their outputs can often not be deteriorated. For Cisos that is a risky proposition.
It is difficult enough to explain cyber security risks to the board; Try to explain why an opaque model has marked a criticism incident – or is not marked. AI effectiveness is one thing, but trusting AI and his processes is something that needs to be planned and must be cultivated over time.
Put things in context
Context is everything in cyber security. Ai can detect an anomaly, but can it see if that anomaly is benign, malignant or even expected? That requires more than pattern recognition. It requires a deep understanding of system base lines, user behavior, network topology and operational rhythms.
Without this basis, AI tools are inevitably susceptible to incorrect interpretation: routine administrative scripts such as threats, or worse, overlooking subtle indicators of compromise that do not meet well -known attack patterns. That creates more trivial work for security teams, because it is up to them to find out what is real and what is not.
This is where the visibility of the network comes into play. AI needs telemetry from every layer of the environment: endpoints, servers, cloud -windowloads, authentication flows, network traffic and more. And it needs data to be correlated, not quiet. A warning from an end point is only useful when it is viewed in addition to what is happening in the system.
A login of an unusual location can be suspected unless it comes from a well -known travel route for a senior executive or a new external rent in a different time zone. Ai cannot make those judgments alone. Without a uniform context, even the most advanced algorithms guess. And in cyber security, guessing is always a liability.
The case for unification
If AI starts playing a meaningful role in cyber security, it first needs a basis that can trust it, and the people who trust it. That starts with visibility, but it extends to the architecture. Freagmented tools with partial views and patented, closed Source Alert Logica only hinders cyber security efforts.
What Cisos need is a coherent detection layer and response in which telemetry is united, logic is transparent and automation is tightly tailored to the operational context. This is where architectural convergence-for example, merging visibility at SIEM level with the orchestration possibilities of extensive detection and response (XDR) becomes critical. This basic line turns AI into a power more for security teams when it is used correctly.
Equally important is explanatory. If an AI system marks a potential threat, security teams must understand why. Not only to validate the warning, but to learn from it, to adjust processes and to communicate risks to leaders and stakeholders. Black-box models may seem impressive, but in security, opacity is a threat vector in itself.
Cisos do not need magic; They need clarity. And the best AI implementations are that those people inform-improve decision-making, speed up triage and popping up the insights that matter the most without drowning teams in noise.
We have put together a list with the best identity management software.
This article is produced as part of the TechRadarpro expert insight channel, where today we have the best and smartest spirits in the technology industry. The views expressed here are those of the author and are not necessarily those of TechRadarpro or Future PLC. If you are interested in contributing to find out more here: https://www.techradar.com/news/submit-your-story-techradar-pro
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