The challenges and benefits of AI for security
Imagine a hyper-intelligent guard dog. He sees everything, hears everything, learns at lightning speed, can open doors on command, distract burglars and is social too. Sounds like every security guard’s dream, right?
But suppose this same guard dog sometimes pulls his own plan, is not always explainable in his choices, or barks at the wrong people. Or worse: bites. Would you still deploy him just like that?
That watchdog is like AI in the world of cybersecurity.
We are at a tipping point. AI is presented as the gamechanger for security. And yes, the possibilities are impressive. But the same technology also brings new risks, blind spots and ethical dilemmas. In this blog, as a preview of our upcoming event, I will take you through the main benefits and challenges of AI for Security. And why Security for AI is at least as important.
What AI can do for security
AI in security has long since ceased to be a pipe dream. It is now part of almost every modern security architecture. From endpoint software to network detection, SIEM, XDR and cloud security, AI is playing an increasing role. Often without you immediately realising it.
The power of AI lies in pattern recognition. Where a human might miss an anomaly, AI recognises the bigger picture. Such as a creeping privilege escalation, anomalous behaviour on the network, or a zero-day attack disguised as regular traffic.
In addition, AI is increasingly responding autonomously. It can automatically isolate a user, end a session or start a playbook. Thus, it increases the resilience of organisations, even when team capacity is limited.
AI also takes over repetitive tasks: summarising incidents, preparing reports or even generating scripts. This gives analysts space to focus on where they really add value. AI makes connections that simply remain invisible to humans and provides insights that structurally improve your security policy.
The flip side: if AI barks too often or stays quiet instead
AI is powerful, but not flawless. An overzealous AI that continuously sounds alarms quickly causes alert fatigue.
False positives are a well-known problem. Reports about behaviour that seems suspicious but ultimately turns out to be innocent. If AI is trained on limited or one-sided data, the system quickly strikes out. Then your SOC runs the risk of losing its overview exactly when something does go wrong.
The same AI that is meant to reduce workloads can actually add to the workload if the setup is incorrect. The more irrelevant reports, the more time you lose verifying them. And the more likely you are to miss a real threat.
Moreover, AI is often a black box. The system blocks traffic or isolates users, but without a clear explanation. This is problematic for security teams who need to be accountable, especially in sectors where audits and compliance weigh heavily.
The reliability of AI hinges on the quality of the input data. Incorrect, biased or contaminated data lead to wrong decisions. In the worst case scenario, attackers actively manipulate your AI system, e.g. via data poisoning or prompt injection.
And criminals use AI just as much. Think automated phishing, deepfakes and large-scale social engineering. The speed and complexity of attacks are increasing. Only adaptive and intelligent defences then offer protection.
A growing concern is also the invisible use of AI in your organisation. More and more employees are using tools like ChatGPT or Copilot without supervision. This so-called ‘Shadow AI’ carries risks, especially if there is no policy and visibility on data use.
Security for AI
We often talk about how AI strengthens security. But how do you keep AI itself secure?
AI is increasingly integrated into critical processes: customer interaction, data processing, code generation. This makes it a full-fledged actor within your IT environment. Just as endpoints, identities and networks need to be secured, so does AI.
How do you tackle this?
Start with awareness. Where is AI already being used, consciously or unconsciously? Set clear frameworks. That means: draw up policies for the use of generative AI, deploy tools to gain insight into data use, and work with models that are explainable. But also: integration with your existing security architecture. Think of links with your SOAR, XDR or NDR platform, so that AI is a reinforcement of your existing resilience.
Those who integrate AI strategically gain speed, scale and insight. Those who deploy it uncontrollably run risks that only become visible when it is too late.