TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management
  in LLM-based Agentic Multi-Agent Systems
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.
6840f32dda736de98e843831
Shaina Raza
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2025-06-05T01:39:24.184Z
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Shaina Raza
user
shainar
6840f32dda736de98e843832
Ranjan Sapkota
claimed_verified
2025-06-27T08:58:06.919Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
Ranjan Sapkota
user
RanjanSapkota
6840f32dda736de98e843833
Manoj Karkee
6840f32dda736de98e843834
Christos Emmanouilidis
TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management
  in LLM-based Agentic Multi-Agent Systems
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.
A review of trust, risk, and security management in LLM-based agentic multi-agent systems, examining governance, explainability, ModelOps, and privacy/security.
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