Urvesh

Summary

• Brings extensive expertise across AI security, security operations, threat hunting, detection engineering, threat intelligence, and Digital Forensics and Incident Response.
• Currently focuses on securing LLM-powered applications, AI agents, and Model Context Protocol ecosystems.
• Has hands-on experience analysing integrations between AI systems, enterprise tools, APIs, data sources, and security platforms.
• Identifies emerging attack surfaces and trust boundaries introduced by AI-powered applications and agentic workflows.
• Experienced in assessing AI security risks such as prompt injection, tool poisoning, excessive agency, insecure tool design, and sensitive data exposure.
• Works with authentication weaknesses, AI supply chain risks, and the detection of suspicious AI and agent activity.
• Brings a strong security operations perspective through practical experience in SIEM, XDR, DFIR, and detection engineering.
• Builds, integrates, attacks, and defends MCP servers across offensive and defensive security use cases.
• Applies MCP to SIEM and log analysis, threat intelligence enrichment, MISP, incident response, and SOAR automation.
• Combines AI security, offensive testing, and defensive engineering to support the secure adoption of MCP in modern security environments.
• Has trained more than 300 cybersecurity professionals through hands-on, application-focused learning programs.
• Focuses on helping participants develop practical skills that can be directly implemented within security teams and enterprise environments.

SKILLS

• AI Security and LLM Application Security
• Model Context Protocol (MCP) Security
• AI Agent Security
• Prompt Injection and Tool Poisoning
• AI Security Testing and Defensive Engineering
• Threat Hunting and Threat Intelligence
• Detection Engineering
• Digital Forensics and Incident Response (DFIR)
• Security Information and Event Management (SIEM)
• Security Orchestration, Automation, and Response (SOAR)

TRAINING EXPERTISE

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