As cyber attacks become faster and more scalable, it can be tempting to think that the solution is even more artificial intelligence (AI). But the real competitive advantage does not lie in new and advanced models. It lies in the ability to use AI correctly – with context, reliable data and an understanding of the reality in which the technology must work.
In just a few years, AI has become a central part of cybersecurity. New and more specialised models can identify vulnerabilities, analyse attack paths and show how vulnerabilities can be exploited. Yet better AI doesn’t automatically mean better defences.
This is because cybersecurity is not just about what can theoretically go wrong. It is about what actually happens in a specific organisation and whether it poses a real risk. That difference is crucial. A robust cybersecurity strategy must take into account the context of environments, data quality, operational expertise and the ability to use AI correctly, as well as the technology itself.
Context is the difference between noise and risk
An AI model can be powerful at finding vulnerabilities and recognising patterns. But it only becomes useful when it understands the environment in which it operates in.
What looks suspicious in one place may be completely normal in another. Employee behaviour, network traffic, cloud activity and day-to-day IT routines differ from company to company. Without that understanding, it becomes difficult to determine whether an incident is a real risk or just an unnecessary alert.
Therefore, context is not an extra detail. This is the very prerequisite for using AI effectively in cybersecurity. The technology must continuously learn how the company works and what is normal in that particular network.
A SOC requires more than standalone AI tools
Many security vendors today talk about AI-powered tools. But the important thing is not whether a tool has AI built in; it’s whether it is actually connected to the rest of the safety work.
A Security Operations Centre (SOC) does not become stronger by having more isolated AI tools. On the contrary, this can actually lead to more complexity, more alarms and more disruption if the tools don’t interact with each other.
Effective cybersecurity requires a comprehensive approach. Data, workflows, models and people must support each other. Of course, AI agents can help sort and prioritise alerts, find signs of threats, handle security incidents and prepare reporting. But they must have clearly defined tasks. Not all of these require the largest and most advanced models either. For some purposes, a smaller model is sufficient if it simply needs to find and extract certain information. More complex tasks, on the other hand, require models that can reason and explain how they arrive at a result.
Regardless of the model type, the quality depends on clear instructions, relevant data and clear boundaries for when AI can act on its own and when humans must be involved. The goal is not to automate everything, but to use AI where the technology can provide improved visibility, better prioritisation and faster decisions.
Humans still need to accompany AI
AI can ease the pressure on security teams performing repetitive and time-consuming tasks. But that doesn’t make people any less important. In reality their role is changing. Security analysts must increasingly assess whether the AI’s conclusions make sense. They must be able to see if the reasoning holds, if the result can be used and if the model begins to deviate over time.
Therefore, clear boundaries, random checks and continuous quality assurance become important. If the limits are set too low, the risk of errors increases. If they are set too high, there is a risk that AI will simply move the problem from too many alarms to too many notifications.
This is not a sign of weakness in AI. That’s how technology should be used. In complex security environments, the interaction between people and technology is necessary to arrive at reliable results.
The defender’s advantage lies in their own environment
AI doesn’t just make defenders stronger. Attackers also use the technology to improve phishing, find vulnerabilities and misconfigurations, develop attack code and automate parts of the attack chain.
This puts increasing pressure on security teams. The time from a vulnerability is discovered until it can be exploited is getting shorter. This means that organisations have less time to react and address gaps in the network. Speed is therefore crucial. Large amounts of data must be able to be processed quickly, and threats must be prioritised before they develop. But speed must not come at the expense of governance, trust or operational discipline.
Context makes the difference in practice
While AI offers attackers new opportunities, defenders have one key advantage: they know their own environments.
They know which systems are critical. They know what normal behaviour looks like, and which deviations cause concern. That knowledge can become a real competitive advantage if combined with good data, a mature security platform and human expertise.
Future cybersecurity will therefore not be determined by who has the most advanced AI model. It will be determined by who can best translate AI into concrete and reliable decisions. More AI is not in itself the answer. The difference lies in the context, the data quality and the ability to use the technology correctly.
Dan Schiappa
Dan Schiappa is President of Technology and Services at Arctic Wolf. A seasoned technology executive, Dan has extensive experience leading businesses from startups (PictureVision, Vingage) to scaled companies (Microsoft, Oracle, EMC/RSA, Sophos).
