For years, security teams have battled alert fatigue, writes Keven Knight, pictured, CEO of Talion Cyber Security.
Security tools generate huge volumes of alerts every day, many of which require investigation, validation and prioritisation before a decision can be made. The challenge is making sense of this data quickly to reduce risk. However, agentic AI is beginning to make a real difference with tackling this challenge. By automatically investigating alerts, correlating signals across multiple systems and applying contextual analysis, these platforms can significantly reduce the number of incidents that require manual review. Analysts spend less time triaging low-value alerts and more time focusing on genuine threats. On the surface, this sounds like a straightforward win. However, the reality is more nuanced.
Risk doesn’t disappear
Reducing alert volumes is often seen as the ultimate goal of security operations. However, when AI starts determining which alerts matter and which do not, the challenge doesn’t disappear. It simply moves. Traditionally, analysts reviewed alerts and applied context before deciding what action should be taken. While that process could be slow, there was a clear line of accountability. Decisions were visible, understandable and made by people.
Agentic AI changes this model. Increasingly, systems are filtering alerts, prioritising incidents and in some cases initiating actions before a human analyst becomes involved. The result is greater efficiency, but it also means important decisions are being made earlier in the workflow.
Visibility mattersย ย
The more responsibility AI takes on, the more important transparency becomes. If an alert is deprioritised, suppressed or classified as low risk, security teams need confidence that the decision was justified. Otherwise, organisations risk creating blind spots within their own security operations.
This becomes particularly challenging in modern environments where multiple security tools, data sources and AI systems are contributing to the same outcome. Each platform may interpret signals differently. Each may apply its own logic, assumptions and contextual understanding. By the time a final decision is reached, it may be the result of several interconnected systems rather than a single, easily understood process. Without proper governance, tracing how that decision was made can become increasingly difficult.
Governance new priority
As organisations adopt agentic AI, governance is rapidly becoming just as important as detection and response. The conversation therefore should not only focus on how many alerts have been removed from an analyst’s queue. It should also focus on the logic determining which alerts reach that queue in the first place.
Future SOCย
Alert reduction is often presented as a measure of success, but it is really a redistribution of decision-making across the security operation. As AI takes greater responsibility for filtering, prioritising and responding to events, organisations need confidence that these processes remain transparent, consistent and aligned with their approach to risk. The most successful organisations will be those that look beyond efficiency gains and focus on trust, visibility and governance. They will understand not only the outcomes produced by their AI systems, but also how those outcomes were reached.
Ultimately, the goal is not simply to reduce noise. It is to ensure that nothing important is lost in the process, and that every decision, whether it leads to action or inaction, can be understood, explained, trusted and justified.
Practical governance
Organisations do not need to sacrifice efficiency to maintain confidence in their security operations. The most resilient organisations recognise that as agentic AI assumes greater responsibility for filtering and prioritising alerts, governance must move upstream alongside those decisions.
Practical steps include:
When governance extends to the earliest stages of AI-assisted decision-making, organisations gain far more than reduced alert fatigue. They build security operations that combine operational efficiency with transparency, accountability, and confidence, ensuring that every decision, including those that prevent an alert from reaching an analyst, can be understood, validated, and trusted.
