As organizations deploy increasingly autonomous AI agents, existing network security weaknesses could allow these systems to operate beyond their intended responsibilities.
Traditional AI safeguards may not adequately address these risks, particularly when network access policies are overly permissive.
Recent joint guidance from NIST, CISA, and the NSA addresses incident readiness and response for highly capable AI systems.
I interviewed Dan Rheault, director of product management at FireMon, via email about what organizations can learn from the guidance and how security teams should prepare their networks before granting AI agents greater autonomy.
AI agents amplify existing network security weaknesses
"An agent’s intended behavior is not an enforceable security boundary," Rheault explained, emphasizing that organizations should not confuse an agent's instructions with independently enforced security controls.
Although the federal guidance primarily addresses advanced AI training and evaluation, Rheault believes its underlying security principles apply to enterprise deployments.
AI agents introduce additional risk because they can make autonomous decisions while operating with potentially broad access to enterprise systems.
Their ability to execute actions at machine speed further increases the potential consequences of a mistake. A single instruction could trigger multiple actions across interconnected systems, extending the impact beyond the intended workflow.
For example, an AI agent responsible for generating reports might also have network access to production administration interfaces. Even if the agent never uses those connections, their availability creates unnecessary exposure.
Rheault recommends examining potential access paths rather than relying exclusively on observed network traffic. Security teams need to understand which systems an agent could reach, not just which systems it has already accessed.
Overly permissive policies expand the potential attack surface
Years of accumulated firewall rules and outdated access policies can create significant challenges when organizations introduce autonomous AI agents. Undocumented network connections can further complicate efforts to identify unnecessary exposure.
An agent assigned to retrieve inventory information might inherit access to an entire application subnet, potentially exposing unrelated databases or management interfaces.
Network connectivity alone does not authorize an agent to modify those systems. However, excessive application privileges could enable unauthorized actions, especially when exposed credentials or exploitable vulnerabilities are also present.
Rheault recommends mapping each agent's business purpose to the network paths and services required to complete its assigned tasks. Security teams should also identify the necessary identities and permissions before granting access.
Testing should extend beyond confirming that an agent can complete its assigned responsibilities.
"Successful task completion proves the agent has enough access. Negative testing confirms it doesn't have too much," Rheault said.
Traditional network security needs additional enforcement layers
Traditional firewalls and network controls remain relevant, but Rheault cautioned that they cannot independently determine whether every action an AI agent performs is authorized.
An agent might legitimately connect to an approved API while attempting an operation outside its assigned responsibilities.
Organizations therefore need coordinated security controls that restrict network access and enforce identity-based permissions. Application authorization and tool-level restrictions provide additional safeguards against unauthorized actions.
Hybrid environments complicate enforcement because cloud security controls may differ from traditional firewall policies. Workload-level policies can introduce further inconsistencies in how access is managed.
Security teams should establish clear relationships between approved business activities and agent identities. These relationships should also define the application permissions and network connections each agent requires.
Incident containment must be tested before deployment
The joint guidance emphasizes verifying containment before affected systems resume operations.
Rheault recommends preparing capabilities to isolate affected workloads and restrict outbound network connections. Security teams should also be able to stop agent execution and revoke relevant credentials to prevent further unauthorized activity.
Security teams must confirm that firewall changes terminate existing connections rather than assuming updated rules immediately block all traffic.
Before restarting an affected agent, organizations should reproduce the problematic behavior in an isolated environment. They must verify that prohibited connections fail without disrupting legitimate operations.
Teams should also confirm that monitoring alerts function correctly. Additional testing should verify that alternative network paths cannot bypass the updated security restrictions.
Three priorities for securing autonomous AI agents
Rheault identified three priorities organizations should address before deploying autonomous AI agents:
- Establish visibility: Document each agent's owner and business purpose. Identify its execution environment and identity, then map required dependencies and approved network destinations. Investigate unexplained access paths.
- Enforce access boundaries: Apply least-privilege network restrictions alongside identity-based permissions. Establish application authorization and tool-level controls to prevent unauthorized actions. Test that approved activities succeed while prohibited activities fail.
- Continuously validate containment: Measure how quickly security teams can detect suspicious activity and isolate affected agents. Verify that prohibited connections terminate, including existing sessions. Regularly rehearse recovery procedures to confirm that containment controls remain effective.
Bottom line
I believe the broader lesson is that AI security and governance cannot depend exclusively on controlling how agents are instructed to behave. As organizations grant agents greater autonomy, security teams must independently enforce their access boundaries and continuously verify that those controls work as intended.
For additional guidance, review our AI Agent Safety Checklist to assess the security controls organizations should have in place before deploying autonomous agents.