Research & Frameworks
Advancing Security for Agentic and Autonomous AI Systems
Ravindra Annam's research focuses on practical security architectures, threat-modeling approaches, and runtime controls for AI agents, LLM-powered applications, and autonomous enterprise systems. His work explores how organizations can move beyond static security controls toward continuous, context-aware protection for increasingly autonomous AI.
Then add these four framework blocks:
RAAI Runtime Security Model™
Runtime Security Architecture for Agentic AI
A runtime-focused security model for governing autonomous AI systems beyond traditional authentication and static authorization. The model emphasizes continuous verification, intent validation, policy-driven execution, least-privilege tool access, behavioral monitoring, and controlled autonomous actions.
AI Runtime Threat Matrix
Threat Modeling for Agentic AI
A structured approach for identifying and organizing runtime threats across AI-agent identity, permissions, tools, memory, context, data flows, external systems, and autonomous execution.
Agentic AI Runtime Attack Tree
Mapping Attack Paths Across Autonomous AI Systems
A threat-modeling approach for visualizing how attacks can propagate across prompts, models, memory, tools, APIs, identities, permissions, multi-agent interactions, and downstream actions.
ANNAM Framework
Enterprise AI Security & Governance
A practitioner-oriented framework focused on measurable security and governance controls for enterprise AI systems, bringing together architecture, runtime protection, monitoring, risk management, and operational governance.
Research Focus Areas
Agentic AI Security • Runtime AI Security • AI Threat Modeling • LLM Security • MCP Security • RAG Security • AI Identity & Authorization • AI Memory Security • Tool Governance • Enterprise AI Governance
Research & Technical Collaboration
Interested in discussing AI security research, technical collaboration, practitioner frameworks, or enterprise AI security architecture?