According to the World Economic Forum, Cyber risk outlook in 2026Artificial intelligence (AI) is expected to be the most important factor shaping cybersecurity strategies this year, with 94% of executives surveyed citing it as a force multiplier for both defense and offense.
The report, released on Monday (January 12), highlights how generative AI technologies are expanding the attack surface, contributing to unintended data leaks and more complex exploitation tactics that exceed the capabilities of purely human-driven teams.
AI to prevent cybercrime
Cyber defense is length Focus on repair after loss happen. AI is driving intervention early in the attack cycle by identifying coordinated actions and emerging risk signals before fraud escalates.
As reported by PYMNTS, companies ramping Enhance the use of AI to prevent suspicious activity in the face of heightened risk Shadow AIthird party agent and Apps that can be used open business above to cyber risk.
Security companies and financial institutions are now using machine learning to correlate activity across multiple systems, rather than relying on individual alerts. Group-IB Cyber Fraud Intelligence Platform is an example of this approach. The system analyzes behavioral patterns across participating organizations to identify account takeovers, authorized push payment fraud, and indicators of fraud. money mule Activities while the scheme is still in place developing. Instead of waiting until losses are confirmed, agencies can flag suspicious behavior based on early indicators such as repeated reuse of credentials or low-value test transactions.
Fraud prevention increasingly relies on shared intelligence and behavioral analysis rather than static rules. By correlating signals across platforms, agencies can detect unintended coordinated activity. looks dangerous inside A single organization.
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AI is also extending to visual risk detection. Truepic’s shared intelligence platform Apply machine learning to analyze images and videos submitted as evidence of identity or compliance across multiple organizations. By identifying visual patterns that have been reused or manipulated, the system can flag AI-generated or modified media that may pass manual review.
AI is also being applied at the identity and session level, with behavioral analytics focusing on how users interact with the system rather than what credentials they present. tools like Keystroke dynamics analysis, device fingerprintsession speed tracking, and behavioral biometrics measure signals such as typing rhythm, mouse movements, touchscreen pressure, IP stability, device configuration, and navigation patterns across sessions. These signals help security systems distinguish between legitimate users from There may already be an attacker have Valid credentials, scenario more This problem will become more common as AI phishing and credential harvesting improves.
Predictive AI models extend this approach by detecting emerging fraud patterns. in front A transaction or approval occurs. In documented cases Cited by Group-IBthe financial institution used predictive AI to identify over 1,100 loan application attempts that included biometric images generated or manipulated by AI. tried To circumvent identity verification using deepfake photos.
The system flagged the activity without passing document inspection aloneHowever, it is done by identifying inconsistencies across device reuse, session behavior, application timing, and interaction patterns that deviate from legitimate customer behavior. This allows financial institutions to stop applications before they are approved, rather than discovering fraud after the disbursement.
Use AI to stop crime
AI-powered defense is no longer limited to private fraud platforms. Governments are incorporating AI directly into cyber and economic crimes execution.
The UAE Ministry of Interior deployed AI and advanced analytics within our cybercrime unit support digital and financial crime investigations. Officials say AI systems can help analyze large amounts of digital evidence, identify connections between incidents, and trace the origin of cyber incidents more quickly than manual methods.
At the enterprise level, leading technology providers are incorporating AI into financial crime and security workflows. For example, oracle Purpose AI-based investigative tools assist analysts by gathering evidence, connecting related cases, and highlighting higher-risk activities.
Small businesses are also deploying AI defensively. Cybersecurity companies in the Midwest expand AI tools that Monitor network traffic, email, and user behavior detect Phishing attempts and unauthorized access real time. These systems focus on early detection of anomalies to prevent incidents from escalating.
The increased reliance on AI reflects a simple constraint. That means human analysts can’t keep up with the volume of attacks generated by automated tools. National security agencies, including the UK’s National Cyber Security Center, warn AI will continue to increase the speed and effectiveness of cyber threats until at least 2027, especially in social engineering and fraud.
Enterprise adoption data already reflects this reality. as PYMNTS reported55% of COOs surveyed said they rely on generative AI-driven solutions to improve cybersecurity management.
