Agentic AI Security At Runtime
The Enterprise Playbook for Securing AI Agents in Production
Agentic AI security has become a growing challenge as enterprises move AI agents into production. These agents can reason, retrieve information and take actions across business systems. However, many organisations still lack the security controls needed to manage that autonomy.
According to the new Agentic AI Security at Runtime playbook, 48% of AI agents operate without meaningful security controls. Traditional application security also struggles to protect systems that make decisions and take actions dynamically at runtime.
The playbook examines the security risks that emerge once organisations deploy autonomous AI agents into real-world environments. It also provides practical guidance for security teams that need to protect those agents without slowing AI adoption.
Agentic AI Security Threats at Runtime
The expert team behind the playbook identifies ten major threat categories that security teams should consider when deploying AI agents.
These threats include prompt injection and its variants, tool and permission abuse, memory and context poisoning, and multi-agent trust propagation.
Unlike traditional applications, an AI agent can combine information from several sources before deciding what action to take. As a result, security teams must consider more than vulnerabilities in code or infrastructure.
Attackers may instead manipulate an agent’s instructions, context, memory or access to tools. Therefore, runtime behaviour becomes a critical part of Agentic AI security.
An Agentic AI Security Playbook Built Around Four Pillars
The playbook structures its approach around four operational pillars: Observe, Enforce, Detect, and Respond.
Together, these pillars provide a framework for understanding what AI agents are doing, controlling what they can do, detecting suspicious behaviour and responding when something goes wrong.
The guide also provides framework-specific recommendations for teams using LangChain, CrewAI, AutoGen and other open-source AI agent stacks.
In addition, organisations receive a phased implementation roadmap. This approach allows security teams to introduce controls progressively rather than launch a lengthy 12-month transformation project.
Shadow Agents Expand the Enterprise Attack Surface
The playbook also examines the rise of shadow agents across enterprise environments.
As employees and development teams experiment with agentic AI, organisations may lose visibility into which agents can access corporate systems, sensitive data or external tools.
That visibility gap can increase risk. An agent with excessive permissions, poisoned context or unsafe tool access may create a path into systems that traditional application controls do not adequately monitor.
For that reason, organisations need to understand where agents operate and what permissions they hold before they can build effective Agentic AI security controls.
Why Agentic AI Security Must Focus on Runtime
Runtime is where an AI agent turns reasoning into action.
Security teams therefore need visibility into how agents behave after deployment, including what information they retrieve, which tools they call and what actions they attempt.
The playbook explains why sophisticated attacks can emerge at this stage. It also shows how security teams can build controls that support the safe expansion of enterprise agentic AI programs.
Organisations can download the free Agentic AI Security at Runtime playbook to explore the ten threat categories, runtime security controls and implementation roadmap.
Download the Agentic AI Security at Runtime playbook: https://neuraltrust.ai/guides/agentic-ai-security-at-runtime.