AI in cybersecurity
As digital technologies become more sophisticated, so does the cyber threat landscape. In 2025, the world will continue to face increasingly malicious attacks in the form of ransomware, social engineering, artificial intelligence (AI)-powered deepfakes and disinformation. Apart from causing financial losses – a data breach costs an average of $4.88 million in 2024 – cybersecurity incidents waste huge organizational resources. According to a recent report, the average time to identify and average time to contain a data breach is approximately 270 days.
Conventional security solutions, such as signature-based antivirus software or rule-based intrusion detection, are no match for AI-enabled cyber threats. For illustration, consider polymorphic malware, which not only studies the usage patterns of antivirus tools to write code that escapes detection, but also changes its shape and signature using an encryption key. To be truly effective, cybersecurity solution must be highly proactive and adaptive, as well as able to anticipate and respond to threats as they evolve. Only AI-powered security has these capabilities.
Artificial intelligence and machine learning (ML) algorithms process massive datasets in real-time to identify anomalous patterns, such as atypical surges in activity linked to a cyberattack, that traditional security solutions and human experts might miss. Enabling real-time monitoring of systems, AI security solutions help enterprises detect risks in advance and act quickly to mitigate adverse consequences. And now, with generative AI, organizations can address even more sophisticated threats, such as zero-day attacks, in a timely manner.
Furthermore, gen AI is transforming cybersecurity itself, reimagining a defensive, reactive activity into a proactive, adaptive and data-driven discipline. Besides learning continuously from training data, gen AI models create synthetic data mimicking real-world information to evolve their understanding of cybersecurity risks. In this way, gen AI tools stay on top of emerging threats; they also predict cybersecurity events and suggest the best solution for every scenario to ensure organizations stay resilient even in the face of rising threats.
But AI, while powerful, is not yet perfect. A major challenge is that algorithms frequently lack transparency, veiling the rationale that led them to a particular outcome. Machine learning models are seen as black boxes whose working is hard to understand for even the people who built them.
Other risks of AI, mainly stemming from the quality of data used to train the models, are bias, inaccuracy and performance degradation or drift. This means that enterprises cannot unquestioningly trust AI-powered-and-automated cybersecurity systems. Explainable AI will go a long way in addressing these concerns and positioning the technology as not only the most capable cybersecurity solution, but also the most reliable.
Explainable AI explained
Explainable Artificial Intelligence (XAI) refers to a set of techniques enabling human beings to understand and interpret the output of AI and ML algorithms, deep learning models and neural networks. Apart from challenging the belief that AI sophistication and opacity go hand-in-hand, XAI promises to fundamentally change the relationship between AI and humans by bridging the divide between algorithmic complexity and the need for transparency and trust.
XAI could be a cybersecurity game-changer for the following reasons:
More robust protection: Limited explainability stands in the way of comprehensive testing of AI models, and potentially allows vulnerabilities to creep in. Threats, such as model inversion attacks, which are caused by reverse-engineering AI models, or content manipulation to produce malicious outcomes, thrive on the lack of explainability. Explainable AI addresses this by providing visibility into the exact nature and cause of such attacks, to enhance detection, mitigation and prevention. What’s more, organizations can continuously monitor and improve the performance of existing models, and use those insights to fine-tune future development.
Greater trust and confidence: Since explainable AI systems are easier to understand and interpret, they inspire trust and acceptance among users. XAI simplifies model evaluation and enables organizations to productionize their models with confidence. Explainable AI is especially critical for highly regulated businesses, such as banking or healthcare, where there is a great need for transparency and accountability.
Reliable results: Algorithms trained on low-quality data – data that is inconsistent, incomplete, inaccurate or biased – produce similarly flawed outcomes. XAI mitigates this problem by making the decision-making process transparent. This helps to reduce false positives, making it easier for cybersecurity analysts to validate alerts and focus their attention on genuine threats. It also improves vulnerability management by identifying and prioritizing risks, based on factors such as exploitability and criticality to business, to enable security teams to address the most pressing concerns without delay.
Adherence to regulatory and ethical standards: Explainability is necessary for complying with the provisions of various regulations, such as GDPR (General Data Protection Regulation), which require AI systems to furnish clear explanations for their decisions, so that people impacted by such decisions can challenge them. Further, XAI makes it easier for regulators to audit AI models and verify their compliance with not just legal, but also ethical requirements (for example, privacy protection, personal data use with consent, and non-discriminatory decisions). Explainability also enhances governance by creating transparency and accountability and ensuring that AI and ML models are aligned with all applicable regulations. Consequently, explainable AI is a big driver of responsible AI – AI that is founded on the principles of trust and human-centricity, achieved by prioritizing people’s safety and fundamental rights, and by adhering to legal, social and ethical requirements.
The future beyond security
Cyber resilience and responsible AI are clear and direct outcomes of explainable AI. However, XAI also holds out an enticing prospect of transforming workplace culture and interactions.
Explainable AI can be viewed as an interpreter, which translates the complex workings and decisions of AI models to align them with human cognition. This not only helps human beings to understand the outcomes produced by AI systems but even enables the systems themselves to explain their actions using human-like reasoning. Over time, this could dramatically change human-AI collaboration and possibly inspire hybrid decision-making systems that combine the capabilities of artificial and human intelligence in novel ways. More immediately though, XAI will build people’s trust in AI and ML models to take human-machine collaboration to the next level.
