Secure AI for Banking With Levo’s Unified AI Security Platform

 AI Security for Banking encompasses tools and governance processes that protect AI models, agents and data flows across core banking, payments, risk scoring AI and customer-facing chatbots. It ensures confidentiality, integrity and compliance while enabling innovation.

AI introduces new risks that traditional controls can’t see: untrusted inputs, prompt injection, model drift and AI‑enabled deepfake fraud. Financial institutions face data leaks and regulatory violations when employees use unapproved tools or when models hallucinate. AI security solutions monitor and govern prompts, embeddings and outputs—capabilities missing in WAFs, SIEMs and DLP.

 Attackers weaponize generative AI to create voice and video deepfakes, hyper‑personalized phishing and automated exploits. Deepfake fraud cases surged 1,740% and caused more than $200 M in losses in early 2025. AI can also probe fraud‑detection models to find thresholds and exploit them. Without AI‑aware defenses, banks are vulnerable to large‑scale fraud and reputation damage.

Shadow AI refers to employees using unapproved AI tools like generative AI chatbots, transcription apps or third‑party plug‑ins, without oversight. In banking, over a third of client interactions may already involve AI outside sanctioned systems. This practice leaks sensitive account data, violates PCI and AML rules and undermines governance. Continuous discovery and monitoring are essential.

 Levo inspects prompts and responses at runtime, redacts PCI/PII and account information and enforces role‑based and geographic policies. It detects unauthorized data flows between models, agents and banking APIs, preventing hidden SaaS AI features from exposing data.

 Yes. AI risk models can embed human bias, drift over time or make opaque decisions. Regulators require banks to explain credit scores, AML alerts and fraud flags. Levo’s runtime monitoring and audit logs provide explainability and traceability of prompts, decisions and outcomes, supporting fair lending and model risk management.

Banks often rely on cloud‑based AI services and fintech vendors. The OECD warns that third‑party dependencies can create systemic risk. Levo discovers and inventories every AI tool, monitors data flows to external APIs and enforces vendor‑specific policies. It alerts on suspicious behavior and ensures compliance with NCUA, OCC and EU AI Act requirements.

Multiple frameworks are emerging. The EU AI Act classifies credit scoring as high‑risk and can fine institutions up to €40 M or 7% of revenue. DORA mandates monitoring of ICT and AI incidents, while U.S. regulators like the OCC, SEC and FDIC require model risk management and layered defenses. Levo maps runtime behavior to these requirements, helping banks prove compliance.

Banking chatbots and virtual assistants must protect customer data and comply with PCI and privacy laws. Levo monitors conversations, filters sensitive information and blocks prompt injection or jailbreak attempts. It logs every interaction for audit trails, ensuring that LLM security for banking chatbots meets compliance.

 AML and risk‑scoring AI systems require high sensitivity to detect suspicious activity without generating bias. Levo’s runtime visibility covers models that handle AML/KYC and risk scoring, detecting anomalies, preventing data poisoning and ensuring that only authorized data is used. It supports explainability so that banks can justify risk scores to regulators.

 Levo’s AI red teaming simulates adversarial prompts, data poisoning and deepfake scenarios against fraud engines, credit models and banking chatbots. The platform identifies weaknesses before deployment and recommends control improvements, helping banks meet the BIS, FSB and ECB expectations for robust risk management.

AI agents can trigger actions across CRMs, payment rails or customer-service systems, which makes unsecured agents a high-risk entry point. Banks should implement real-time monitoring, enforce least-privilege access, and block unsafe tool calls to prevent unauthorized transactions or data exposure. AI Security for Banking platforms like Levo provide runtime policy enforcement and oversight to keep AI agents aligned with compliance and operational rules.

AML AI tools and KYC workflows rely on sensitive identity and transaction data, making them targets for data poisoning, prompt manipulation and evasion techniques. AI security adds protections that validate inputs, detect anomalies, and prevent tampered or misleading data from influencing AML alerts. Banks gain higher detection accuracy, fewer false negatives and auditable, regulator-ready AI behavior across all AML models.

Financial institutions should prioritize platforms that offer runtime AI visibility, policy enforcement, sensitive data protection, monitoring across model chains and integration with existing SOC tools. They should also support banking-specific requirements like PCI handling, AML/KYC compliance and explainability for credit scoring AI. Solutions like Levo combine AI threat detection, governance and protection into one unified control layer.

Banking chatbots powered by LLMs must prevent PCI/PII leakage, avoid hallucinated account information and comply with strict customer-communication rules. AI security tools monitor every prompt and response, detect unsafe patterns, and apply guardrails that block unauthorized data or policy violations. This ensures LLM security for banking chatbots while improving trust, accuracy and customer experience.