Supporting Research

AI Security & Risk Management

2 Articles

Security is a major hurdle for the adoption of AI in enterprises. This area includes topics such as red teaming and adversarial testing of large language models (LLMs), identifying attack surfaces in autonomous systems, designing secure agent architectures, employing privacy-preserving AI techniques, preventing jailbreaking, and developing risk assessment frameworks for reasoning systems.

Organizations should learn to evaluate their AI security posture, design a defense-in-depth strategy for autonomous agents, implement security controls for Model-Checking Protocol (MCP)-based orchestration, and create threat models for Enterprise Digital Twins. These practices are essential for organizations deploying AI in environments where security breaches can lead to regulatory, financial, or reputational consequences.

Who This Is For

CISOs (Chief Information Security Officers), Security Leaders Risk Managers,Enterprise Architects, Compliance Teams

Key Topics

  • Red teaming LLMs and reasoning systems
  • Adversarial robustness testing
  • Secure agent architectures
  • Privacy-preserving AI techniques
  • Attack surface analysis for agentic systems
  • Security governance for MCP integration
  • Jailbreaking prevention strategies
  • Threat modeling for AI systems

LLM Red Teaming 2025: A Practical Playbook for Securing Generative AI Systems

July 13, 2025 63 min read

Red Teaming Large Language Models: A Practitioner’s Playbook for Secure GenAI Deployment distills eighteen months of research, incident reports, and on-the-ground lessons into a single, actionable field guide. You’ll get a clear threat taxonomy—confidentiality, integrity, availability, misuse, and societal harms—then walk through scoping, prompt-based probing, function-call abuse, automated fuzzing, and telemetry hooks. A 2025 tooling snapshot highlights open-source workhorses such as PyRIT, DeepTeam, Promptfoo, and Attack Atlas alongside enterprise suites. Blue-team countermeasures, KPI dashboards, and compliance tie-ins map findings to ISO 42001, NIST AI RMF, EU AI Act, SOC 2, and HIPAA. Human factors are not ignored; the playbook outlines steps to prevent burnout and protect psychological safety. A four-week enterprise case study shows theory in action, closing critical leaks before launch. Finish with a ten-point checklist and forward-looking FAQ that prepares security leaders for the next wave of GenAI threats. Stay informed and ahead of adversaries with this concise playbook.

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LLM Observability & Monitoring: Building Safer, Smarter, Scalable GenAI Systems

June 9, 2025 36 min read

Deploying Generative AI into production is not the finish line. It marks the beginning of continuous oversight and optimization. Large Language Models (LLMs) bring operational challenges that go beyond traditional software, including hallucinations, model drift, and unpredictable output behavior. Standard monitoring tools fall short in addressing these complexities. This is where LLM Observability becomes critical, offering real-time visibility and control to ensure reliability, safety, and alignment at scale.

This guide provides a strategic framework for enterprise leaders, AI architects, and practitioners to build and maintain trustworthy GenAI systems. It covers the four foundational pillars of observability: Telemetry, Automated Evaluation, Human-in-the-Loop QA, and Security and Compliance Hooks. With practical tactics and a real-world case study from the financial industry, the article moves beyond high-level advice and into actionable guidance.

If you are working on RAG pipelines, AI copilots, or autonomous agents, this article will help you make your systems production-ready and resilient.

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