Key Takeaways
- Artificial intelligence (AI) is expected to be the most consequential factor shaping cybersecurity strategies in 2026, with 94% of surveyed executives citing it as a force multiplier for both defense and offense.
- AI is expanding the attack surface, contributing to unintended data exposure and more complex exploitation tactics that outpace the capacity of purely human-led teams.
- Companies are ramping up their use of AI to guard against suspicious activities, using machine learning to correlate activity across multiple systems and detect coordinated behavior and emerging risk signals.
- AI is being applied at the identity and session level, using behavioral analytics to distinguish legitimate users from attackers who may already have valid credentials.
- Governments and large technology providers are integrating AI into cybercrime and economic crime enforcement, using AI-driven investigation tools to analyze digital evidence and identify links between cases.
Introduction to AI in Cybersecurity
The World Economic Forum’s Cyber Risk in 2026 outlook highlights the significant impact of artificial intelligence (AI) on cybersecurity strategies. According to the report, AI is expected to be the most consequential factor shaping cybersecurity strategies in 2026, with 94% of surveyed executives citing it as a force multiplier for both defense and offense. As the report notes, "generative AI technologies are expanding the attack surface, contributing to unintended data exposure and more complex exploitation tactics that outpace the capacity of purely human-led teams." This shift towards AI-driven cybersecurity is a response to the increasing complexity and sophistication of cyber threats, which are becoming more difficult for human analysts to keep pace with.
AI for Cybercrime Prevention
AI is being used to prevent cybercrime by identifying coordinated behavior and emerging risk signals before fraud scales. As PYMNTS reported, companies are ramping up their use of AI to guard against suspicious activities, even as they face a rising risk from shadow AI, third-party agents, and apps that could open businesses up to cyber risks. Security firms and financial institutions are using machine learning to correlate activity across multiple systems, rather than relying on isolated alerts. For example, Group-IB’s Cyber Fraud Intelligence Platform analyzes behavioral patterns across participating organizations to identify signs of account takeover, authorized push payment scams, and money-mule activity while schemes are still developing. As the platform’s developers note, "instead of waiting for confirmed losses, institutions can flag suspicious behavior based on early indicators such as repeated credential reuse or low-value test transactions."
Visual Risk Detection
AI is also expanding into visual risk detection, with companies like Truepic using machine learning to analyze images and videos submitted as identity or compliance evidence across multiple organizations. By identifying reused or manipulated visual patterns, the system can flag AI-generated or altered media that might otherwise pass manual review. As Truepic’s CEO notes, "AI is being applied at the identity and session level, where behavioral analytics focus on how a user interacts with a system rather than what credentials they present." This approach has already shown significant results, with financial institutions using predictive AI models to identify over 1,100 attempted loan applications involving AI-generated or manipulated biometric images.
AI-Driven Defense
AI-driven defense is no longer confined to private fraud platforms, with governments integrating AI directly into cybercrime and economic crime enforcement. The UAE Ministry of Interior has deployed AI and advanced analytics within its Cybercrime Combating Department to support investigations into digital and financial crimes. Officials say AI systems help analyze large volumes of digital evidence, identify links between cases, and trace the origins of cyber incidents more quickly than manual methods. As the Ministry’s spokesperson notes, "AI is a game-changer in the fight against cybercrime, allowing us to respond more quickly and effectively to emerging threats."
Enterprise Adoption
The growing reliance on AI reflects a simple constraint: human analysts cannot keep pace with attack volumes generated by automated tools. National security agencies, including the U.K.’s National Cyber Security Centre, warn that AI will continue to increase the speed and effectiveness of cyber threats through at least 2027, particularly in social engineering and fraud. Enterprise adoption data already reflects this reality, with 55% of surveyed chief operating officers saying they are relying on generative AI-driven solutions to improve cybersecurity management. As one COO notes, "AI is no longer a nice-to-have, it’s a must-have in the fight against cybercrime."
Predictive AI Bridges the Security Response Gap in Automated Attacks