AI Threats & Fraud Intelligence

AI-enabled fraud uses synthetic media generation, large language models, and machine learning to create deceptive content — voice, video, text, and identity — that is indistinguishable from genuine human-generated material at the point of verification. Conventional fraud relies on human impersonation, document forgery, and social engineering that trained reviewers can detect through inconsistency and contextual anomaly. AI-enabled fraud bypasses these controls by generating contextually consistent, biometrically plausible synthetic content at scale and at a cost approaching zero per attack.

AI voice cloning uses neural text-to-speech models trained on audio samples of a target's voice to generate new speech in that voice from arbitrary text input. Modern voice cloning models require as little as three seconds of audio — publicly available from earnings calls, interviews, social media, or voicemail greetings — to generate convincing synthetic speech. Voice biometric systems used in banking and enterprise authentication were designed to detect recorded playback attacks, not real-time synthetic voice generation. The acoustic signatures these systems look for as liveness indicators are absent in AI-generated audio, which is why current voice biometric deployments are failing against cloned voice attacks.

Shadow AI refers to AI tools, applications, and embedded AI features used by employees without organisational knowledge, approval, or governance oversight. This includes personal AI assistant subscriptions, AI features embedded in productivity software, departmental SaaS tools with AI capabilities, and AI-powered browser extensions. The security risk is data exposure — employees routinely submit confidential documents, client data, source code, financial information, and regulated personal data to external AI models that process, retain, and potentially train on that data outside any data processing agreement or security control the organisation has in place.

Agentic AI systems take autonomous action — browsing the web, executing code, calling APIs, sending emails, managing files, and making decisions — with delegated credentials and minimal human oversight. Standard AI security concerns focus on data exposure and output quality. Agentic risks include prompt injection attacks that redirect agent behaviour, over-privileged credential abuse, irreversible action execution before human review, lateral movement through connected systems, and data exfiltration through legitimate API calls that bypass conventional DLP controls. Agentic AI introduces a new class of identity and access risk that existing PAM and IAM frameworks were not designed to address.

Detection of synthetic media attacks requires a layered approach combining technical and procedural controls. Technical controls include liveness detection systems that analyse micro-expressions, blinking patterns, and lighting inconsistencies in video; spectral analysis of audio for artefacts characteristic of neural text-to-speech generation; and digital provenance verification using content credentials standards. Procedural controls include out-of-band verification for high-value requests, callback verification to known numbers rather than caller-supplied contacts, and mandatory human review for any financial authorisation or access change request initiated through a remote channel. Neither technical nor procedural controls alone are sufficient — the combination is what current enterprise security architectures are missing.

A synthetic identity is a fabricated persona constructed from a combination of real and fictitious personal data — or entirely AI-generated — with sufficient consistency across biometric, documentary, and behavioural dimensions to pass automated verification controls. In enterprise fraud, synthetic identities are used to create fraudulent employee accounts, bypass KYC onboarding processes, establish vendor relationships, infiltrate contractor and supply chain positions, and conduct hiring fraud where the placed synthetic persona then facilitates data theft or insider access. The North Korean IT worker programme represents the most extensively documented state-sponsored use of synthetic identity for enterprise infiltration to date.