Agentic AI has evolved from a buzzword to a practical tool. Unlike a typical AI Large Language Model (LLM) these systems do more than generate text: They can plan tasks, act on them, and chain tools together autonomously. Essentially, they behave like digital teammates by performing multistep tasks toward specific goals, not just answer prompts.
This new capability changes the security landscape for your business. Many third-party risk management (TPRM) programs still treat AI tools as standard software. They ignore how the autonomy and system access in these tools can create a severe security risk. Organizations that underestimate agentic AI may face operational, financial, and security problems.
Why AI Agents Are High-Privilege Vendors
Autonomous AI agents are becoming part of modern SaaS ecosystems. These agents have access to data, can take actions, and can modify configurations.
AI agents may read production logs, open tickets, modify firewall rules, or spin up cloud resources. But a lot of businesses continue to evaluate such tools in lightweight ways that are intended to provide analytics dashboards or HR systems. This can create fatal loopholes in security.
Classifying Agentic AI in Third Party Risk Management
The conventional third-party risk management programs categorize vendors according to data sensitivity and business impact. In the case of agentic AI, there should be a different level of autonomy and scope of action. For example:
Tier A: Read-Only Copilots
These agents have access to data, but they cannot alter it. They are secure in monitoring, reporting, and analyzing.
Tier B: Suggest-Then-Act Agents
These agents do not implement actions but suggest them. e.g., remediation actions. But the activities are not enforced without human approval. They save on manual work and maintain supervision.
Tier C: Fully Autonomous Operators
These agents can make direct alterations or modifications to cloud systems, identity platforms, and production environments. They are at the greatest risk, and they must be closely monitored.
All layers will vary in their identity, logging and rollback requirements. Finer-grained service accounts, tamper-evident logs, a documented human kill switch, and rollback procedures should be present in Tier C agents.
Key Due Diligence Questions
Standard SOC 2 or Standard ISO 27001 questionnaires are insufficient in agentic AI. Companies should ask:
- What actions can be undertaken by the agent in our environment?
- What are the permissions of its tools and connectors by user or by system?
- Does it have a full audit trail of all the actions?
- What does the vendor do to avoid injecting prematurely, misusing, or objectively drifting?
These questions have started to be included in audits by major companies such as PwC. Nevertheless, they are not fully operationalized in most of the programs and companies that do not do these checks expose themselves.
Practical Steps for Security Teams
The deployment of agentic AI promises several advantages but can also raises cybersecurity risk. The security challenges can be made more difficult with complex setups and inadequate oversight. Therefore, businesses should:
- Consider agentic AI a different type of vendor.
- Modify policies to include risk level and autonomy.
- Modify contracts to deal with identity, logging, and rollback.
- Enter the activity data of the feed agents into continuous monitoring, rather than conducting annual reviews.
This solution helps to strike a good balance between the efficiency advantages of AI agents and the need for high levels of surveillance.
Agentic AI: Not Just Another Tool
Consider a cloud environment in which an AI agent will automatically spin credentials and additional rules in a firewall.
When uncontrolled, it may result in conflicts, misconfigurations, or breaches. A Tier B strategy makes sure that there is a human who approves such actions and the risk is minimized, and time is saved.
To avoid unintended consequences, a Tier C agent would need complete logs, access controls, and a kill switch.
Agentic AI is not just another tool. It is a new type of third-party vendor with autonomy and privileges that require oversight. Treating these agents as standard software ignores the risks and creates vulnerabilities.
By classifying agents by autonomy, asking agent-specific due diligence questions, and updating policies and monitoring, organizations can safely leverage their benefits. Recognizing agentic AI as a separate vendor class is essential to controlling risks while gaining operational value.