{"id":25488,"date":"2025-10-29T07:45:30","date_gmt":"2025-10-29T11:45:30","guid":{"rendered":"https:\/\/www.insightpartners.com\/?p=25488"},"modified":"2025-10-29T07:45:30","modified_gmt":"2025-10-29T11:45:30","slug":"securing-agentic-ai","status":"publish","type":"post","link":"https:\/\/www.insightpartners.com\/ideas\/securing-agentic-ai\/","title":{"rendered":"Securing the autonomous future: Trust, safety, and reliability of agentic AI"},"content":{"rendered":"

In our last post, \u201cThe race to secure enterprise AI<\/a>,\u201d we covered how enterprises are securing AI deployment. As generative AI<\/a> becomes indispensable to business operations, security teams are struggling to keep pace with threats emerging during both model development and runtime, creating a significant bottleneck to successful adoption.<\/p>\n

In this second installment of our three-part series, we dive deeper into the deployment of AI Agents. While AI Agents share some features and security considerations with traditional models, they go a step further \u2014 actively making decisions, taking actions, and interacting with systems or environments to achieve goals autonomously. This evolution raises the stakes for security, requiring enterprises to not only secure the models themselves but also manage agent identity and access, monitor behavior in production, and secure the tooling, code, and data Agents use to function.<\/p>\n

1. AI Agent security as a multi-trillion dollar problem<\/h3>\n

In 2024, our post on “Intelligence-First Design”<\/a> predicted how the rise of AI co-workers and agentic applications would revolutionize software design. As software evolves from merely digitizing industries to augmenting knowledge-based work, transforming labor, and services, the market is expanding beyond its current $650B size, creating a multi-trillion-dollar opportunity<\/a> for founders and enterprises.<\/p>\n

As deployments scale, potential cybersecurity incidents and the opportunity for threat actors to monetize novel attack techniques scale too. And alongside that, the growing threat of AI-driven cybercrime, projected to exceed $15T by 2030<\/a>, underscores the importance of securing AI Agents in production, as many of those attacks will be seeking to exploit business processes that will be increasingly driven by AI co-workers or agentic applications.<\/p>\n

2. Recap of AI Agent architectures and the anatomy of an Agent<\/h3>\n

Our previous post, “State of the AI Agent Ecosystem<\/a>,” examined the emerging application architecture for AI Agents, key enterprise use cases, and challenges. Over the past year, agentic systems have matured significantly, from basic task agents to process agents managing end-to-end workflows with partial autonomy. Enterprises are now deploying role-specific agents with memory and multi-step reasoning, and early adopters are piloting AI employees that own business outcomes through orchestrated multi-agent teams.<\/p>\n

The emergence of AI agent orchestration, reflection, and evaluation as key design patterns has driven the development of new solutions to address these gaps. Moreover, advancements in AI models like GPT5<\/a> and Sonnet 4.1<\/a> can enable them to complete tasks that last nearly an hour, compared to just a few seconds with earlier models like GPT 3.5. As AI Agents increasingly operate autonomously, accessing numerous tools and sensitive internal data, robust identity verification and secure authentication processes have become essential to ensure safe and effective interactions within complex ecosystems.<\/p>\n

3. Where are the opportunities in AI Agent security?<\/h3>\n

Individually, the components that create an AI Agent \u2014 software, LLM, databases \u2014 have established security solutions. But it\u2019s the complexity that\u2019s introduced when combining all these components into a singular system that opens the door for novel technology. Additionally, understanding the intent of data flowing between components requires solutions that will understand whether particular prompts or natural language are part of normal interactions, or potentially harmful or malicious in intent.<\/p>\n

When we look at where the cybersecurity opportunities lie in relation to AI agents, we see five areas ripe for innovation:<\/p>\n

    \n
  1. Managing AI Agent identity and access<\/strong><\/li>\n
  2. Agent governance, observability, and monitoring: <\/strong>The telemetry needed across the components of the AI agent system to manage and monitor agents and then detect and respond to malicious or improper actions.<\/li>\n
  3. Agent integration security: <\/strong>Observability and security across the boundary of an AI Agent system, understanding what actions those agents are taking or being asked to take, and ensuring those fit within the intent of the AI Agent system.<\/li>\n
  4. Novel threats to AI Agents<\/strong><\/li>\n
  5. Data privacy and security:<\/strong> Protecting data throughout its lifecycle and across AI Agent systems.<\/li>\n<\/ol>\n

    Access governance for autonomous AI Agent identities<\/h2>\n

    There are two models for agent identity and access:<\/p>\n