This topic covers AI-driven solutions used to establish content authenticity and stop fraud: face and image recognition, deepfake and synthetic-media detection, account-authentication tools, and the governance and compliance systems that operationalize them. As manipulated images and voice deepfakes become more accessible, organizations must combine edge vision platforms for low-latency, privacy-preserving inference with enterprise security and compliance tooling for detection, response, and auditability. Key categories include Edge AI Vision Platforms (on-device inference for face matching, liveness checks, and real-time tamper detection), AI Security Governance (automated SecOps, observability, and multi-agent orchestration), and Regulatory Compliance Tools (provable provenance, logging, explainability, and evidence for auditors). Representative tools illustrate these roles: Mistral AI supplies efficient, enterprise-first models suitable for on-device or private-cloud vision and multimodal detection; Simbian provides autonomous SecOps agents and a unified Context Lake to detect, reason, and act across incidents; Kore.ai enables orchestration of multi-agent workflows with governance and observability for repeatable detection and response playbooks; Observe.AI’s voice and conversation intelligence capabilities align to contact-center anti‑fraud use cases (voice spoofing and account takeovers); Perplexity AI’s web-grounded answers and developer APIs can support forensic research and sourcing for provenance investigations. Current trends include multimodal detectors combining pixel/audio/metadata signals, edge-first architectures to preserve privacy and reduce latency, AI agents that automate triage and remediation, and stronger regulatory expectations for provenance, explainability and audit trails. Practical deployments balance detection accuracy, operational integration, and documented controls to satisfy both security and compliance requirements.