Over the last decade, artificial intelligence has gone from being a futuristic concept to something actively used in everyday security operations. About 67 percent of organisations use AI‑based tools in their security operations to enhance threat detection and response, showing how widely AI technology has been adopted in security systems. Companies now deploy AI-driven tools (like CrowdStrike Falcon, Darktrace, and Vectra AI) to watch over their networks, manage cyberattacks, security incidents and even warn them about possible breaches before they happen.
These tools promise faster detection, fewer mistakes, and security systems that improve as they learn. For organisations handling sensitive data or running critical services, that promise feels reassuring. But as AI takes on more responsibility protecting infrastructure and personal information, a difficult question keeps coming up: can we truly trust AI to keep us safe?
This article explores both sides of that question: the growing confidence in AI-driven security, and the real concerns around errors, bias, and overdependence on automation.
The Rise of AI in Security
Artificial intelligence has changed cybersecurity in ways that would have seemed unrealistic just a few years ago. Older security systems mostly worked with fixed rules. They compared activity against known threat signatures and raised alarms when something looked unfamiliar. That method worked when attacks followed predictable patterns.
Machine Learning and AI introduced a different approach. Instead of relying only on what is already known, AI uses pattern recognition and machine learning to study behaviour over time. It can analyse millions of events in real time and spot anomalies that suggest something is wrong, even if the attack itself is new.
AI-powered tools now appear across multiple layers of cybersecurity:
- Next-generation firewalls use machine learning to spot unusual traffic patterns.
- Endpoint protection systems use AI to detect ransomware or malware before it executes.
- Network monitoring systems apply AI to identify unauthorised access attempts.
These tools improve speed, reduce manual work, and help small security teams manage tens of thousands of alerts every day, far more than humans could realistically handle on their own. Survey data shows that many organisations are no longer just testing AI but actively adopting it as a core part of their cybersecurity strategy.
For defenders, the promise is appealing. In late 2025, a Stanford study showed how an AI agent called ARTEMIS was able to uncover vulnerabilities in a complex academic network at a much lower cost than human experts, while achieving similar results.
Still, progress in detection and automation does not mean AI is flawless or fully trustworthy. Behind the success stories are deeper concerns that deserve attention.
When AI Makes Mistakes
One of the most common problems with AI is accuracy. AI systems make decisions based on data and models, and both can be imperfect.
In real-world testing and live environments, AI security tools still produce false positives and false negatives. False positives occur when normal activity is flagged as malicious, disrupting work and causing unnecessary alarm. False negatives are more serious, as real threats slip through unnoticed. Both types of errors come with real financial and operational costs.
Bias is another issue. AI systems learn from past data, and that data is not always complete or fair. When certain people, locations, or behaviours are missing or poorly represented, the system can make wrong assumptions. As a result, AI may wrongly flag normal activity as suspicious simply because it has not seen enough of it before.
There is also the transparency problem. Sometimes AI tools deliver results without showing how they reached them. Security analysts may see a security alert, such as a blocked login or a file flagged as malicious, but not the reasoning behind it. When something goes wrong, this lack of visibility would make it difficult to review the decision or understand what caused the failure.
This matters because understanding the decision making process and being able to question them are sometimes important as results themselves.
The Human Element in AI Security
Some people assume AI will eventually replace human security analysts entirely. Most professionals working in cybersecurity do not see it that way.
In industry surveys, only about 30 percent of respondents say their security teams actively use AI tools in daily workflows. Many others are still testing or evaluating them. A significant number have expressed concern about relying too heavily on automation and prefer a more careful approach with strong human oversight.
This caution reflects an important reality. Giving AI more responsibility does not automatically improve security if the people using those tools do not understand how they work, what they can miss, or where their limits are.
In real incident response situations, AI can surface thousands of alerts every hour. If security teams lack the skills to evaluate those alerts, or do not understand why certain threats were prioritised, serious breaches can still happen. Humans remain essential for interpreting context, making strategic decisions, and knowing when automation needs to step aside.
The Dark Side of Automated Security
While AI helps defenders, it also benefits attackers. Cybercriminals now use AI to scale attacks, generate convincing phishing messages, and automate the process of finding weak systems. Some state-sponsored groups have gone even further, using AI to automate penetration testing and attack campaigns with minimal human input.
AI-generated deepfakes add another layer of risk. Attackers can use fake audio or video to impersonate executives, bypass authentication, or manipulate public opinion. Detecting these attacks is difficult, especially as the technology improves faster than many detection tools.
AI models themselves are also targets. Prompt injection attacks, where carefully crafted inputs manipulate an AI’s behaviour, have become a growing concern. These attacks can cause systems to act in unintended ways, sometimes with serious consequences.
Security professionals now talk about adversarial AI, a situation where the same tools built for protection are repurposed for harm. When AI is used on both sides, trust becomes harder to define.
Overdependence and the Illusion of Safety
Another challenge is overdependence.
Organisations that rely too heavily on AI may reduce investment in human expertise or neglect basic security practices, assuming automation will cover the gaps. This creates an illusion of safety.
AI tools are often sold as simple solutions, but security environments are rarely simple. Each organisation has different systems, risks, and priorities. A one-size-fits-all AI system cannot adapt perfectly without careful setup and constant oversight.
Shadow AI adds to the problem. Employees often use unauthorised AI tools for work without security teams knowing. These tools can introduce new risks that automation alone cannot detect. This not only creates hidden risks but also expands the organisation’s attack surface in ways that automation alone cannot detect or control.
Without proper governance and skilled people managing them, AI systems can make organisations feel secure while leaving serious vulnerabilities untouched.
Building Trustworthy AI Security
Trust in AI security does not happen automatically. It has to be built deliberately.
Several principles shape trustworthy AI systems:
- Explainability: AI decisions should be understandable to the people managing them. When systems are opaque, mistakes can grow unnoticed.
- Human oversight: AI should support human teams, not replace them. Critical decisions should always allow room for human intervention.
- Continuous monitoring: Threats evolve quickly. AI models must be reviewed and updated regularly to stay effective.
- Bias mitigation: AI security tools learn from records of past logins, network activity, and cyberattacks. That information needs to cover a wide range of users and environments to avoid biased or inaccurate outcomes.
Industry frameworks like the NIST AI Risk Management Framework reflect these principles and offer guidance for responsible adoption.
Real-World Security Stories
In one widely reported case, attackers used AI-powered automation to run an attack end to end. The activity slipped past existing defences, and systems believed to be safe were still compromised.
At the same time, AI has helped security teams spot threats that older tools would probably have missed. Some organisations say it has shortened response times and made it easier to see attack patterns buried deep in large volumes of data.
Together, these examples point to the same issue: the technology that helps uncover hidden risks can also be turned into a tool for creating them.
The Middle Path Forward
Trusting AI in security isn’t about picking humans or machines, but about how they work together. AI can process huge amounts of data quickly, but humans bring judgement, context, and ethical thinking. Organisations that combine both human expertise and AI, backed by clear oversight and strong governance, are far better positioned than those that treat AI as a magic fix.
As security gets more automated, collaboration becomes essential. AI should be a capable partner, helping humans do more without replacing critical decisions. Trust won’t come automatically, it has to be earned through transparency, accountability, and careful human supervision. Only then can we be sure AI is really keeping us safe, and not just making us feel safe.
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