title: TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management
    in LLM-based Agentic Multi-Agent Systems
summary: Agentic AI systems, built on large language models (LLMs) and deployed in
  multi-agent configurations, are redefining intelligent autonomy, collaboration
  and decision-making across enterprise and societal domains. This review
  presents a structured analysis of Trust, Risk, and Security Management (TRiSM)
  in the context of LLM-based agentic multi-agent systems (AMAS). We begin by
  examining the conceptual foundations of agentic AI, its architectural
  differences from traditional AI agents, and the emerging system designs that
  enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is
  then detailed through four pillars governance, explainability, ModelOps, and
  privacy/security each contextualized for agentic LLMs. We identify unique
  threat vectors and introduce a comprehensive risk taxonomy for the agentic AI
  applications, supported by case studies illustrating real-world
  vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms,
  transparency and oversight techniques, and state-of-the-art explainability
  strategies in distributed LLM agent systems. Additionally, metrics for
  evaluating trust, interpretability, and human-centered performance are reviewed
  alongside open benchmarking challenges. Security and privacy are addressed
  through encryption, adversarial defense, and compliance with evolving AI
  regulations. The paper concludes with a roadmap for responsible agentic AI,
  proposing research directions to align emerging multi-agent systems with robust
  TRiSM principles for safe, accountable, and transparent deployment.
paper_authors._id: 6840f32dda736de98e843831
paper_authors.name: Shaina Raza
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-05T01:39:24.184Z
paper_authors._id: 64d3c16a0553a2522f1aa792
paper_authors.avatarUrl: /avatars/951e272ffccf2388f138b248e5ef7142.svg
paper_authors.fullname: Shaina Raza
paper_authors.type: user
paper_authors.user: shainar
paper_authors._id: 6840f32dda736de98e843832
paper_authors.name: Ranjan Sapkota
paper_authors._id: 6840f32dda736de98e843833
paper_authors.name: Manoj Karkee
paper_authors._id: 6840f32dda736de98e843834
paper_authors.name: Christos Emmanouilidis
paper_title: TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management
    in LLM-based Agentic Multi-Agent Systems
paper_summary: Agentic AI systems, built on large language models (LLMs) and deployed in
  multi-agent configurations, are redefining intelligent autonomy, collaboration
  and decision-making across enterprise and societal domains. This review
  presents a structured analysis of Trust, Risk, and Security Management (TRiSM)
  in the context of LLM-based agentic multi-agent systems (AMAS). We begin by
  examining the conceptual foundations of agentic AI, its architectural
  differences from traditional AI agents, and the emerging system designs that
  enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is
  then detailed through four pillars governance, explainability, ModelOps, and
  privacy/security each contextualized for agentic LLMs. We identify unique
  threat vectors and introduce a comprehensive risk taxonomy for the agentic AI
  applications, supported by case studies illustrating real-world
  vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms,
  transparency and oversight techniques, and state-of-the-art explainability
  strategies in distributed LLM agent systems. Additionally, metrics for
  evaluating trust, interpretability, and human-centered performance are reviewed
  alongside open benchmarking challenges. Security and privacy are addressed
  through encryption, adversarial defense, and compliance with evolving AI
  regulations. The paper concludes with a roadmap for responsible agentic AI,
  proposing research directions to align emerging multi-agent systems with robust
  TRiSM principles for safe, accountable, and transparent deployment.
paper_ai_summary: A review of trust, risk, and security management in LLM-based agentic multi-agent systems, examining governance, explainability, ModelOps, and privacy/security.
paper_ai_keywords: LLMs agentic AI multi-agent systems TRiSM governance explainability ModelOps privacy security encryption adversarial defense compliance AI regulations trust-building mechanisms transparency oversight interpretability human-centered performance benchmarking responsible AI research directions