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Deepfake technologies, powered by generative AI and large language models, are increasingly being used by cybercriminals to create convincing fake images, videos, and audio for fraudulent purposes. These deepfakes can cause significant reputational and financial harm to businesses, and the rapid advancement and accessibility of AI tools have lowered the barrier for threat actors to launch such attacks. As a result, organizations face heightened risks from scams that leverage synthetic media to deceive employees and customers, making detection and prevention more challenging.
Artificial intelligence models designed to detect fraud, particularly in real-time payment systems, struggle to identify scams that rely on social engineering and human deception. While AI can flag statistical anomalies in transaction patterns, it often fails to catch authorized push payment scams where victims are manipulated into making seemingly normal transfers. The inability of current AI systems to discern intent or detect sophisticated social engineering tactics highlights a critical gap in enterprise defenses against modern, AI-enabled scams and deepfake attacks.
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AI is accelerating and scaling social engineering by automating reconnaissance, targeting, and victim engagement, reducing both the cost and skill required to run convincing phishing and fraud campaigns. One reported evolution is the use of **AI agents** to collect open-source intelligence and conduct live, interactive conversations with targets with minimal or no human involvement, enabling high-volume, continuously running scam operations that can adapt in real time. Deepfake-enabled impersonation is further eroding trust in voice and video communications, including calls and meetings, with examples cited of finance staff being deceived into transferring **millions** after interacting with fabricated “executives.” Recommended mitigations emphasize shifting from human-sense validation to process-based controls—e.g., enforced verification procedures, out-of-band checks, shared authentication phrases (“safe words”), and emerging *content provenance* approaches—because traditional, predictable detection models are increasingly strained by the speed, personalization, and adaptability of AI-driven attacks.
Jun 29, 2026
AI technologies are rapidly transforming the landscape of cybercrime, enabling scammers to create highly convincing deepfakes and personalized attacks that are increasingly difficult for individuals and organizations to detect. Recent research and industry reports highlight a surge in AI-powered scams, with over 70% of consumers encountering scams in the past year and deepfake audio and video emerging as top concerns. Attackers are leveraging social media as a primary channel to target victims, exploiting the widespread use of mobile devices, which often lack adequate security protections. The sophistication of these attacks is exemplified by incidents such as the $25 million fraud at Arup, where a deepfaked videoconference deceived an employee into transferring company funds. The growing threat of deepfakes and synthetic media is driving a cybersecurity arms race, as organizations struggle to keep pace with evolving attack techniques. Security leaders are increasingly focused on strengthening identity controls, as insurers now scrutinize the maturity and enforcement of identity and access management practices before offering coverage. Research also reveals that current identity document verification systems are hampered by limited and non-diverse training data, making them vulnerable to advanced fraud tactics. As AI continues to lower the barrier for attackers, both technical and human-centric defenses must adapt to counter the risks posed by synthetic identities and technology-enhanced social engineering.
Jun 29, 2026
Banks and payment providers are warning that **deepfakes** and agentic AI are increasing fraud pressure on digital payments, particularly where identity verification, authorization, and anti-money-laundering controls depend on signals that can be spoofed. Deloitte highlighted rising concern that AI-generated voice and video can be used to impersonate customers and defeat banking verification workflows, while other industry reporting noted warnings that deepfakes could bypass AML and identity checks and expose institutions to higher account takeover and payment fraud risk. At the same time, firms are adapting payment infrastructure for AI-driven commerce rather than allowing autonomous models to move money without guardrails. Industry analysis said payments remain difficult for AI agents because settlement requires licensed intermediaries, deterministic execution, regulatory compliance, and clear liability for fraud and errors. Providers are responding with agent-focused controls such as wallet-based authorization, token protections, and abuse detection, reflecting a broader shift toward **constrained, supervised AI payments** instead of fully autonomous transaction execution.
Aug 4, 2026