AI Platforms for Cybersecurity & Fraud Detection covers the software stack and workflows that use LLMs, agent frameworks, analytics engines and domain detectors to identify and mitigate fraud, abuse and model-level threats. The topic spans AI Security Governance (policy, model monitoring and provenance), Regulatory Compliance Tools (AML/KYC workflows, audit trails), AI Data Platforms (ingestion and feature stores for telemetry and transactions) and Data Analytics Tools (real-time dashboards and ML explainability). Relevance is driven by three parallel trends: increasing volume of machine-generated fraud (phishing, automated spam and conversational scams), continued exposures in smart-contract and blockchain code, and stronger regulatory scrutiny of automated decision systems. Defenses range from CertiK-style static and formal analysis of contracts to AMLBot-style transaction monitoring pipelines and SpamGPT-style detection models that identify model-generated abuse. Key tools play complementary roles: IBM watsonx Assistant can host virtual incident-response agents and automated investigation playbooks; LangChain provides the engineering primitives to build, test and orchestrate agentic detectors and pipelines; Sisense supplies embedded BI and analytics for alerting and executive reporting; Microsoft 365 Copilot helps analysts summarize incidents and streamline compliance documentation; Observe.AI adds voice and conversation-intelligence for contact-center fraud detection; Dataisland ingests documents and evidence to train domain-aware assistants and preserve institutional knowledge. Taken together, these platforms emphasize orchestration, data lineage, explainability and operationalization — not just model accuracy. Effective deployments combine real-time analytics, agent-driven automation, and governance controls to reduce false positives, speed investigations and produce auditable trails for regulators.