-optimized.jpg){kind=avatar}
Organisations are moving faster to adopt agentic AI than they are to govern it. This imbalance may become one of the defining cyber risks of the next decade, says Keven Knight, CEO, Talion Cyber Security.
The industry has spent years focused on improving visibility into threats, reducing response times and increasing automation. Agentic AI promises to accelerate all three. However, speed alone has never been the measure of effective security. Organisations must also understand how decisions are being made, who remains accountable for them and whether they align with business objectives. As AI becomes more embedded within security operations, visibility into decisions will become just as important as visibility into threats.
Transparency and accountability are foundational in security operations. Security teams need to be transparent about the threats they are facing and they need to remain accountable for the decisions they make to safeguard the business they are hired to protect. Without accountability and transparency, confidence in security is a mirage.
No one can understand how safe the organisation really is, and no one can be sure that everything wonโt collapse at any moment. However, even despite these risks, many organisations have unintentionally lost accountability and transparency within their security operations. The reason for this is AI.
AI in the SOC: From efficiency to responsibility
Many organisations today are using AI in their Security Operations Centre (SOC), however, over time these machines have evolved from supporting roles to key decision influencers.
AI was initially introduced to reduce workload, improve efficiency, or respond more effectively to the growing volume of alerts, but over time it has started to take on more responsibility, moving beyond supporting analysis and into shaping how incidents are interpreted and handled. Yet this decision making is often made under the hood of AI systems, meaning humans canโt see the reasoning behind conclusions. For years, the structure of the SOC has been relatively stable. Alerts are generated, analysts investigate, and decisions are made based on experience, context, and judgement. Even as detection capabilities have improved through platforms, the underlying model has remained centred on human interpretation.
However, Agentic AI alters the model, particularly as systems move from presenting information to actively interpreting it. These systems correlate signals across environments, apply logic, and in some cases initiate actions. Decisions are no longer made exclusively by individuals. They are increasingly influenced, and at times executed, by systems operating at a scale and speed that traditional models cannot match.
Most organisations believe this is an efficiency gain, but what they are actually encountering, often without fully realising it, is a system beginning to influence key decisions. However, when modern agentic systems begin to influence decisions, the need for clarity around how those decisions are made becomes more pronounced. Decisions may be made more quickly, but without a clear understanding of how they were reached or who is accountable for them, confidence in security erodes.
Governing the AI enabled security operations
For organisations looking to tackle this critical challenge, they need to look for SOC partners that clearly define how decisions are structured and executed at scale , enabling security operations to interpret and act on data as it occurs across complex, multi-system environments.
Overall, organisations must ensure AI is governed in practice, and that all decisions are made, validated, and aligned with risk. What is required is not just a faster SOC driven by agentic AI, but a fundamentally different operating environment, where the emphasis shifts from processing alerts to structuring decisions in a way that can be understood, governed, and trusted.
The organisations that understand the importance of this are beginning to adapt how decisions are made, ensuring that systems operate within frameworks that provide clarity, consistency, and accountability. The organisations that do not take action to tackle the issue will risk allowing autonomous capability to advance ahead of human-led control, creating conditions where actions can be taken more quickly than they can be explained or validated.
The implication is not that organisations should slow adoption, but that they must approach it with greater intent, treating decision-making as the central design challenge rather than an outcome of tooling. When agentic AI is introduced within a structured and governed model, it becomes a force multiplier that strengthens resilience and improves consistency. When it is not, it introduces a layer of opacity that can undermine confidence in the very systems it is intended to enhance.
The organisations that will benefit most from this transition are those that recognise that speed is not the objective on its own, but a byproduct of clarity, structure, and control. The real advantage is not simply the ability to act faster, but having the transparency and accountability to understand, justify, and stand behind every security decision that is made.
Practical governanceย ย
Organisations do not need to choose between speed and accountability. The most effective security operations are designing governance directly into their AI operating models. Practical steps include:
When these controls are embedded into operational workflows, organisations can benefit from the speed of agentic AI while retaining the visibility, accountability, and confidence required to manage cyber risk effectively.
Photo by Mark Rowe: Street art, Belfast.
