How do I build an RFP for an agentic AI security platform tailored to a regulated financial services company?

An effective RFP for an agentic AI security platform in financial services should evaluate how the platform controls autonomous AI behavior at runtime, not only whether it passes compliance checklists. Financial services organizations face regulatory exposure from prompt injection, unauthorized tool execution, sensitive data exfiltration, and autonomous workflows that bypass intended controls.

The RFP should be organized around these evaluation areas:

Discovery and inventory

• Continuous discovery of AI agents, MCP servers, prompts, APIs, and LLM applications across cloud and hybrid infrastructure

• Automatic detection of shadow AI and unmanaged agent deployments

Runtime controls

• Behavioral monitoring of autonomous agent actions in production

• Inline enforcement that can block unsafe actions before execution

• MCP proxy controls for traffic inspection and policy enforcement

Security validation

• Continuous AI red teaming against prompt injection, tool misuse, privilege escalation, and unsafe action chaining

• Validation under real attack conditions, not only static scans

Governance and compliance

• PII and secrets detection across agent inputs and outputs

• Audit logging with policy traceability mapped to regulatory frameworks

• Role-based access controls for AI system governance

• Multi-cloud support with SIEM and CI/CD integration

Akto provides a purpose-built foundation for regulated AI environments. ATLAS, Akto's employee AI security product, governs employee AI usage, shadow AI, and browser-based interactions with inline guardrails. ARGUS, Akto's runtime agent monitoring product, secures internally built AI agents and MCP-connected systems with continuous behavioral monitoring and enforcement. Executive dashboards surface policy coverage, exploit attempts, guardrail performance, and sensitive data events that map directly to regulatory reporting requirements.