AI deepfake and voice‑phishing detection focuses on identifying synthetic or manipulated audio and conversational fraud in real time, protecting customer identity and preventing social-engineering attacks. Rising fidelity of generative voice models and increased use of voice channels for account access have made vishing and voice deepfakes a persistent operational and regulatory risk for contact centers, financial services, and large enterprises. Organizations now need detection that is fast, explainable, auditable, and integrable with incident-response workflows. Key approaches combine conversation intelligence, voice biometrics/anomaly detection, and automated SecOps orchestration. Modulate’s ToxMod and VoiceVault offer real‑time voice-native moderation and voice fraud/scam detection to block or flag suspicious calls at the point of interaction. Observe.AI brings enterprise conversation intelligence and GenAI for contact centers—real‑time assist, post‑call QA, and VoiceAI agents—enabling detection signals to feed customer‑facing workflows and QA pipelines. Simbian layers autonomous AI security agents and a unified Context Lake to detect, reason, and act—helping SecOps teams triage voice‑fraud alerts, automate hunts, and maintain an evidence trail. Duckie provides no‑code autonomous support agents that can act on detection outputs (e.g., block transactions, open tickets, or escalate for human review) across channels. Together, these categories address prevention (real‑time blocking), detection (conversation analytics and anomaly scoring), and response (orchestration and human‑in‑loop review). Current priorities for deployment are minimizing false positives, preserving privacy and explainability, documenting audit trails for compliance, and integrating detection signals into incident response and customer workflows.