Key Takeaways
- Agentic AI introduces new security risks by enabling AI systems to independently reason, make decisions, access enterprise applications, APIs, cloud resources, and sensitive data.
- Least-privilege access and secure AI identities help prevent excessive permissions, unauthorized access, and misuse of enterprise resources.
- Continuous behavioral monitoring can identify unusual API activity, privilege abuse, abnormal data access, and other deviations that may indicate compromised Agentic AIs.
- Human oversight remains essential for high-risk actions, while prompt injection protection, restricted API access, encryption, logging, and regular security testing strengthen agent security.
- Fidelis strengthens agentic AI security through integrated XDR, NDR, endpoint security, deception technology, and cyber terrain mapping for visibility, behavioral analytics, threat detection, and investigation across hybrid environments.
Artificial intelligence is now moving into a new generation where systems do not only respond, but they can also plan, reason, and independently perform complicated tasks. This new breed of independent systems, dubbed “agentic AI,” is reshaping enterprise operations as fast as it can. Agentic AI is being used in various aspects of digital transformation, from streamlining security procedures and overseeing cloud systems to streamlining business workflows and supporting financial operations.
However, greater autonomy also brings greater responsibility. Unlike traditional AI models that can analyze or generate information, agentic AI systems can make real-world decisions and act via enterprise applications, APIs, cloud resources, and sensitive business information. Compromised Agentic AIs or agents without security measures can easily expose, alter, or exfiltrate sensitive data, engage in unauthorized activity, or intensify cyberattacks.
In an increasingly autonomous era of AI, effective agentic AI security measures are essential. Security teams need to be aware of the distinct risks, put in place governance policies, and implement continuous monitoring to make sure that agentic AI are safe and responsible. According to Gartner[1], by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.
What is Agentic AI?
Agentic AI is a type of AI system that can reason, plan, make decisions, and execute actions with very little human input. These Agentic AIs don’t just tackle a single prompt, but also divide large objectives into smaller tasks, communicate with enterprise applications, and adjust their actions to the evolving situation.
AI can now:
- Create and run multi-step processes
- Interact with enterprise applications via APIs.
- Evaluate data in a structured and unstructured format.
- Communicate with cloud solutions.
- Work cooperatively with other agentic AI
- Draw lessons from past results to enhance future performance
These can range from AI-powered SOC analysts, cloud infrastructure assistants, procurement agents, software development copilots, customer service agents, to financial automation assistants. Agentic AI security is of paramount concern for CISOs and security leaders, as these systems can have a direct impact on enterprise operations.
- Generates High-Confidence Alerts
- Disrupts Autonomous and AI-Assisted Attacks
- Extends Detection Across Hybrid Environments
Agentic AI Security Best Practices
To keep the autonomous AI systems safe, a multi-layered security strategy is required, involving governance, identity protection, behavior monitoring, and human supervision. Let’s explore some of the best practices for securely deploying agentic AI in an enterprise environment.
1. Apply Least-Privilege Access
Agentic AIs should only be granted access to the necessary permissions to perform assigned tasks. The potential of an agent to compromise is greatly magnified if the privileges are excessive. To prevent Agentic AIs from accessing resources they don’t need, organizations should use role-based access control (RBAC), attribute-based access control (ABAC), just-in-time privilege elevation and temporary credentials.
2. Secure Agent Identities
Each Agentic AI needs to have a secured enterprise identity that is managed by enterprise identity security controls. On a frequent basis, security teams should disable keys, store secrets in secure vaults, monitor authentication activity, and ensure that they employ strict identity lifecycle management.
3. Monitor Agent Behavior
Since autonomous AI is always making decisions, organizations will need to look at how AI behaves as well as looking for signatures. Some of the key indicators include unexpected API activity, execution of unusual tools, privilege abuse, unusual data access, unusual network communications, and deviations from typical operations. Continuous monitoring helps to identify new threats from agentic ai quickly and before they reach full scale.
4. Protect Against Prompt Injection
The use of prompts should be incorporated into AI security strategies by default, including prompt validation, sanitization, and verification. However, securing agentic AI also requires limiting what an agent can do if its instructions are manipulated. Organizations should enforce least-privilege access, authorize and restrict tool use, isolate agents from untrusted content and environments, validate high-risk actions, establish execution boundaries, and require human approval for consequential actions. These controls reduce the opportunities attackers have to influence autonomous decision-making and limit the potential impact of a compromised agent.
5. Restrict Tool Access
Not all Agentic AIs need access to enterprise applications. Organizations need to consider carefully which tools each agent really has a need for and restrict access as necessary. Restricted usage of plugins, whitelisting approved APIs, segmenting production environments and requiring an approval for sensitive operations limit the attack surface in the organization.
6. Require Human Approval for High-Risk Actions
Agentic AIs can handle numerous tasks on their own, but it is important that some require human consent. Before performing an operation, such as making a financial transaction, changing security policies, deleting sensitive data or infrastructure changes, there should be a workflow that requires approval. Human supervision ensures that no costly error or misuse of compromised agents take place.
7. Protect Sensitive Data
Agentic AIs often handle sensitive company info, such as consumer data, credentials, and IP. Data should be encrypted at rest and in transit; sensitive data should be tokenized if possible, and there should be data loss prevention (DLP) policies in place. Agent data is protected, which helps prevent data from being exposed if an agent or system is compromised.
8. Maintain Audit Trails
All actions taken by an Agentic AI need to be recorded for security, compliance, and investigation purposes. Any prompts, tool usage, API calls, authentication events, permission changes, and data access activities should be logged in an audit log. In-depth logging can help security teams piece together incidents, detect suspicious activities, and comply with regulations.
9. Conduct Security Testing on Agents
Security assessments need to go beyond the traditional application to Agentic AIs and their workflows. Organizations need to perform configuration audits, penetration testing, adversarial simulation, red team exercises, and prompt injection testing to help uncover vulnerabilities in advance and prevent attackers from exploiting them. Continuous testing ensures Agentic AIs are resilient as models, tools, and environments change.
10. Implement Policies and Procedures
Creating governance policies for deploying, managing and retiring Agentic AIs. The policies should specify what is considered acceptable use, ownership, risk classification, security expectations, compliance expectations, and incident response. A strong governance system ensures efficient operation, alignment with business objectives, and compliance with security and regulatory standards of Agentic AIs.
Agentic AI Security Frameworks
Organizations should not be developing new governance structures but facilitate autonomous AI deployments alongside existing agentic AI security governance frameworks, which enable risk management and continuous improvement.
Commonly used frameworks are:
- NIST AI Risk Management Framework (AI RMF)
A voluntary framework for managing AI risks and promoting trustworthy, secure, and responsible AI throughout the system lifecycle. It provides guidance across Govern, Map, Measure, and Manage functions. - NIST Cybersecurity Framework (CSF)
A framework for helping organizations identify, assess, manage, and reduce cybersecurity risks. CSF 2.0 provides a flexible approach that can also support security practices around AI-enabled systems. - OWASP Top 10 for Large Language Model Applications
A security guidance resource that highlights critical risks in LLM and generative AI applications, including prompt injection, insecure output handling, sensitive information disclosure, and excessive agency. - MITRE ATLAS
A living knowledge base that maps adversary tactics and techniques targeting AI-enabled systems. It can support AI threat modeling, security assessments, and red-team exercises, including threats to agentic AI. - ISO/IEC 42001 – Artificial Intelligence Management System
An international standard for establishing, implementing, maintaining, and continually improving an AI Management System (AIMS). It provides a structured approach to managing AI-related risks and governance. - ISO/IEC 27001 – Information Security Management System
An international standard for establishing and continuously improving an Information Security Management System (ISMS). It provides a risk-based approach to protecting information and strengthening organizational security controls.
They offer defined guidance for governance, risk, AI development, identity management, monitoring, compliance, and incident responses throughout the AI lifecycle.
SOC Agentic AIs and Security Operations Automation
Security Operations Centers are increasingly using autonomous AI to improve operational efficiency. AI can enhance analysts’ security operations automation by enriching alerts, correlating threat intelligence, prioritizing incidents, recommending investigations, and automating repetitive workflows via soc AI agents.
Agentic AI isn’t intended to replace human analysts, but to act as force multipliers to speed up investigations and minimize alert fatigue. While AI can significantly reduce the time and effort required for these tasks, human oversight is still crucial, especially for decisions involving high risk, incident response, and the implementation of the AI-generated recommendations within the organization’s security policies. AI-powered SOC operations, combined with continuous monitoring and behavioral analytics, can play a major role in enhancing enterprise cyber resilience.
How Fidelis Security Strengthens Agentic AI Security
While AI and large language models (LLMs) are being introduced across enterprise applications, organizations require more than traditional cybersecurity measures. AI systems can make decisions on their own, access confidential information, and communicate with business applications, leading to potential conflicts of interests, over privileging, and insider-like actions. With continual visibility, behavioral analytics and advanced threat detection, Fidelis Security helps organizations recognize and reduce these risks.
The integrated XDR, NDR, endpoint security, deception technology and cyber terrain mapping capabilities of Fidelis give deep visibility across cloud, network, endpoint and hybrid environments. This allows security teams to track the activity of the Agentic AI, detect unusual activity, investigate unauthorized use, and identify any signs of compromised or misaligned AI before it disrupts business operations.
Key capabilities include:
- Detecting observable behavioral anomalies that may indicate compromise, misuse, manipulation, or unexpected agent activity.
- Continuously monitor insider-like AI activity by detecting unusual actions, data access, and lateral movement.
- Integrate AI-based threats at cloud, network, and endpoints to speed response times and investigation.
- Keep a continuous eye on AI governance and analyze behaviors and proactively hunt threats.
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- Detect Advanced Threats Before Damage Escalates Trusted
- Cybersecurity Leader for 20+ Years
- See why security teams choose us over other solutions
Conclusion
Agentic AI is one of the most promising developments in enterprise tech, and it allows autonomous agents to reason, plan and operate increasingly complex business actions. But more autonomy means more attack surface, identity risks, prompt injection vulnerabilities and governance issues that demand organizations to rethink their traditional cyber security strategies.
Securing autonomous AI isn’t something that can be achieved by simply employing individual security solutions. They need to adopt robust agentic AI security policies, encompassing identity management, least-privilege access, behavioral monitoring, secure governance structures, continual threat monitoring, and regular human intervention for critical decisions.
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