AI is now penetrating businesses faster than any other technology in the last decade. It creates application code, writes text, analyzes data, summarizes meetings, and speeds up everyday work. Executives see more productivity, employees see less routine.
But this is only the visible part of the change.
Something more fundamental is happening beneath the surface – the risk of sensitive data leaks, identity misuse and rapid, automated attacks is growing. AI handles identities, permissions and data exactly as you set them up.
Crucially, AI also amplifies the abilities of attackers. If access, data and logs are not controlled, there is room for fast, automated and hard-to-detect attacks.
When the user has too many accesses, so does the AI
Forgotten roles, historically added permissions, documents shared “for all” – the AI will go through and use it all. Just the way it is in the company. No corrections. No limits.
This is not an AI problem. This is an identity and access control problem.
AI just multiplies reality. If there is a mess in the approaches, AI won’t improve it – it will just open it up on a larger scale. Productivity flies up, but the risk of data leaks and identity misuse flies with it.
Therefore, the main question today is not “how to deploy AI”, but “do we have AI attitudes and behaviours under real scrutiny?”. AI, agents and automated workflows need to be actively monitored, their decision-making audited and control over what data and resources they access.
Quality of security is based on quality of data
While the problem in productivity is an overabundance of permissions, the opposite is often the case in security – a lack of quality, connected data.
Without central logging, without long-term retention, without correlation of identity, endpoint, email and network events, investigating a security incident will be like putting together a puzzle with missing pieces. The timeline will be incomplete. The cause of the incident will remain unknown. Decisions will be based on weak foundations.
Here, we come across one of the key security trends of 2026:
log management and auditability of (not only) AI systems.
Log management as a basis for modern security
Robust log management is no longer a “nice to have”. It becomes the central nervous system of the security environment.
But it doesn’t end with the centralization of logs. Advanced SIEM/SOAR platforms allow you to ingest and correlate data from identities, endpoints, email services, applications, network elements. Over such consolidated logs it is possible to:
- evaluate anomalous user behaviour,
- to understand the course of the attack inits entire context across multiple systems,
- classify the incident according to MITRE ATT&CK,
- automate the response,
- and perhaps even use AI to dramatically speed up investigations.
SOC needs quality data, which will be supplied by the SIEM. Only on top of this can the security analyst do real business impact analysis and make appropriate decisions.
AI is not a substitute for SOC analytics. It needs it.
The use of AI in incident analysis can significantly speed up the entire process while automating the initial defensive steps – from immediate detection and alerting of an attack to limiting its impact. For now, though, it still needs a human to:
- knows the environment, knows which systems are critical,
- understands the real workings of company processes,
- has responsibility and can make decisions with regard to impacts.
Without a human context, AI remains merely a tool that suggests possibilities. Only the analyst will make the right decision from them.
SecuRadar: when a SIEM is not just a license, but a managed model
Many organizations purchase a SIEM solution and expect to have solved security. However, the license itself does not provide correlation, properly configured detections or continuous monitoring.
The key is:
- how the data is connected,
- how the detection scenarios are set up,
- how incidents are classified,
- how quickly the incident escalates,
- who’s actually investigating him.
SecuRadar represents a model where the SIEM is not an isolated tool but part of a managed security service. It is built on top of Microsoft Defender XDR / Sentinel technologies, automatically works with logs from Microsoft 365, but also allows integration of other data sources.It includes long-term log retention, reporting and advanced anomaly detection, incident classification according to the MITRE ATT&CK framework and clearly defined SLA responses.
In practice, this means that the organisation gains:
- centralised visibility across applications, identity, endpoints and the network,
- quality data basis for analytics,
- continuous monitoring,
- and incident handling processes.
And it is only in such an environment that Security Copilot begins to deliver real value. Because it works on high quality, structured and correlated data.
What the company should really address in 2026
The main question is not:
“What new AI tool will we deploy?”
The correct question is:
Do we have AI under control?
Do we know what approaches are actually used? What is happening in our environment?
Do we have a quality SIEM as a data backbone?
Do we have a SOC, technology and analysts that can respond properly in the event of a security incident?
