This chapter outlines the increasing cybersecurity risks in digital and AI-driven finance while focusing on robo-advisors. This chapter contextualises robo-advisory architecture, threat taxonomy and entry points, and how AI presence adds to cybersecurity attack surface area. Then it moves into a deeper analysis of key attack vectors like insecure APIs, misconfigured cloud infrastructure, and exposed data pipelines as common entry points. Building on this, the chapter examines advanced attack techniques, including adversarial machine learning methods such as data poisoning, model evasion, and backdoor insertion, targeting AI decision-making reliability. These techniques exploit AI model complexity, highlighting the need for explainable AI (xAI). Advanced Persistent Threats increasingly deploy these for long-term unauthorised access. Furthermore, the chapter investigates systemic vulnerabilities from interconnected digital ecosystems, including cloud services and distributed computing. Decentralised finance (DeFi) asset risks like smart contract exploits, flash loan attacks, and oracle manipulation are given special attention as novel threats. The chapter also addresses the growing importance of data privacy in AI-powered financial services, especially for robo-advisors handling sensitive client data. It examines increasing regulatory scrutiny and emphasises the need for compliance, transparency, and robust ethical standards. Ethical considerations like algorithmic bias and trust are integrated throughout. Towards the end, this chapter also explores the role played by AI and xAI applications in forensic accounting and auditing for fraud detection and investigations. It concludes with strategic recommendations for securing digital wealth management platforms, encompassing advanced encryption, multi-factor authentication, zero-trust architecture, AI-driven anomaly detection, and secure software development and robust data governance.