Security for AI Applications — Protecting LLMs, Agents & Mobile AI Systems in BFSI

Security for AI refers to the set of practices, tools, and frameworks designed to protect artificial intelligence systems—including large language models (LLMs), agentic AI, and ML pipelines—from threats like adversarial attacks, prompt injection, model poisoning, and data exfiltration. In BFSI, AI security also encompasses regulatory compliance, ensuring that AI-driven financial applications meet standards such as ISO 42001, PCI DSS, and RBI/SEBI mandates.

LLMs deployed in BFSI face threats including prompt injection attacks, jailbreaking attempts, adversarial inputs designed to manipulate financial decisions, data exfiltration through model outputs, and model inversion attacks that expose sensitive training data. Agentic AI systems face additional risks such as goal hijacking and tool misuse, which can result in unauthorized financial transactions or compliance violations.

AI Red Teaming specifically targets the unique vulnerabilities of AI systems—such as LLM prompt manipulation, agent goal hijacking, and adversarial ML attacks—rather than conventional network or application vulnerabilities. It uses automated adversarial testing to simulate realistic attack scenarios against your AI models and workflows, identifying weaknesses before malicious actors can exploit them in live financial environments.

ML Model Scanning is a zero-trust verification process that inspects AI and ML models for tampering, hidden backdoors, and supply chain compromises before deployment. In BFSI, where models influence credit decisions, fraud detection, and customer interactions, a compromised model can cause catastrophic financial and reputational damage. Model scanning ensures only integrity-verified models are used in production.

Protectt.ai secures mobile AI applications through Runtime Application Self-Protection (RASP) with 100+ deep-tech security features, including protection against runtime hooking, app reverse engineering, AI model extraction, and device-level fraud. Our lightweight SDK integrates seamlessly into Android and iOS banking and fintech apps, delivering real-time threat detection without compromising application performance or user experience.

Protectt.ai's AI security platform supports compliance with ISO 42001 (AI Management Systems), ISO 27001 (Information Security), PCI DSS (Payment Card Industry), ISO 22301 (Business Continuity), RBI's Cyber Resilience and Digital Payment Security Controls, SEBI's Cybersecurity and Cyber Resilience Framework, and NPCI Security Controls. Automated compliance reporting reduces audit preparation time by up to 90%.

Yes. Protectt.ai's solutions are delivered as lightweight, easy-to-integrate SDKs and API-based integrations, designed for rapid deployment into existing mobile banking apps, payment platforms, and AI workflows with minimal operational overhead. The platform supports cloud-based and on-premise deployments, making it compatible with diverse BFSI technology stacks without requiring significant re-architecture.

Protectt.ai's AI/ML-powered threat detection is specifically engineered for low false-positive rates, ensuring that legitimate AI model interactions and user transactions are not incorrectly flagged or disrupted. The behavioral-driven approach and continuously adaptive intelligence models learn normal AI system behavior, so alerts are highly accurate and actionable, reducing alert fatigue for security teams in financial institutions.