Client Architect, Networking
Agentic AI refers to software that can plan, act, and iterate toward an objective with limited supervision—often by chaining tools such as web access, code execution, ticketing systems, cloud consoles, and configuration platforms. In cybersecurity, these capabilities can improve speed and coverage; however, they also give adversaries a practical way to scale reconnaissance and intrusion workflows.
For the purpose of this article, let’s focus on network infrastructure; firewalls, routers, switches, IDS/IPS, Wi‑Fi, etc. Since these systems concentrate privilege on the management plane and are increasingly operated through centralized orchestrators and APIs, a compromised or misaligned agent can reroute traffic, weaken segmentation, and reduce telemetry visibility at scale.
How Agentic AI is Changing Network Risk
Agentic AI accelerates how quickly existing weaknesses are identified, tested, and exploited across the network. For instance:
- Agentic AI turns network intrusion into an automated, iterative process—accelerating discovery of exposed management surfaces and credential-driven access attempts
- Centralized network controllers and APIs amplify impact: one stolen token or mis-scoped role can enable fleet-wide configuration drift in minutes
- Network controls can be “quietly” degraded (selective rule exceptions, NAT/routing adjustments, reduced logging) without creating obvious outages—delaying detection and response
- Detection programs face dual pressure: adversaries can probe controls for evasion, while over-automated tuning can suppress high-signal alerts
Effective risk reduction is achievable without halting innovation by isolating the management plane, constraining automation privileges, and building rapid rollback into change pipelines.
Why Agentic AI Changes the Network Risk Profile
Agentic AI reduces the human-time bottleneck in intrusion workflows by automating scan → validate → iterate loops. For network teams, this means faster enumeration of exposed management surfaces, more targeted credential attacks, and quicker pivots when controls block initial attempts. In practice, small differences—Internet exposure, MFA enforcement, and admin ACLs—often determine whether remote compromise is feasible
Example: Public reporting described AI-assisted compromises of exposed Fortinet FortiGate firewall instances that relied primarily on weak credentials and exposed ports—an illustration of how agentic tooling can scale access against perimeter controls.
Network operations are increasingly centralized (controllers, APIs, IaC, GitOps), which improves consistency—but also concentrates risk. If credentials are stolen or an overly broad role is granted, an attacker can enumerate assets and execute low-friction changes at scale. The result is increased blast radius: a single token can enable rapid configuration reads/writes, and task-driven agents may take unsafe shortcuts (e.g., widening access or disabling inspection) to complete objectives.
Where the Network Is Most Exposed
Firewalls and secure gateways: These platforms unify remote access, segmentation, and inspection—making them high-leverage targets. Agentic AI increases the likelihood of “silent weakening,” where minimally disruptive edits (small exceptions, shadow NAT, downgraded inspection, reduced logging) persist long enough to enable follow-on access while avoiding obvious service impact.
Example: Firewall configuration backups can expose VPN parameters, certificates, admin users, and routing intent, enabling rapid mapping of trust boundaries and higher-confidence pivots.
Routers and Switches: With management access, an attacker (or erroneous agent) can change routing, VLANs, ACLs, or QoS to intercept, degrade, or isolate communications. Whereas poorly governed automation can trigger instability and broad availability impact.
IDS/IPS and network detection: Detection depends on stable baselines and trustworthy telemetry. Agentic AI can probe and adapt traffic until it falls below thresholds (timing, protocol options, encryption, and destinations), effectively learning what “looks normal.”
Example: Some reporting noted that AI platforms can be abused as intermediaries for encrypted command-and-control (C2), complicating detection because egress may appear to be routine access to a popular service.
WiFi and NAC: Wireless access control blends authentication, onboarding, and segmentation. Agentic AI can accelerate discovery and abuse of controller/NAC policy surfaces and can also drive policy erosion via “temporary” operational workarounds that later become persistent.
Recommended Controls for Network Leaders
The following controls reduce systemic risk while still enabling teams to benefit from automation and AI-assisted operations.
- Isolate and monitor the management plane
- Strengthen identity, MFA, and secret hygiene (including agent credentials)
- Constrain automation privileges and API scopes
- Require auditable changes with staged rollout and rollback
- Protect telemetry integrity (logs, detections, and retention)
- Segment agent runtimes and constrain egress for AI tooling
- Rate-limit bulk changes; maintain kill switches for rapid containment
- Run tabletop exercises for agentic change and credential-compromise scenarios
Closing Perspective
Agentic AI raises network exposure primarily by accelerating familiar issues;
Exposed management surfaces, weak credentials, and over-privileged automation (ex:SNMP).
AI does this all at machine speeds, often faster than monitoring tools can detect.
These exploits are not new, nor native to agentic AI. Organizations that understand this, and that the network is a critical security boundary, will be better positioned to deal with these threats as they mature. This allows organizations to enforce least privilege, strong identity, and observable change before their enterprises are adversely affected by this growing risk.
At DataEndure, our engineering teams are constantly evaluating the latest technologies in networking, security, and infrastructure to ensure we can offer the best possible solution to fit your needs. If you’re evaluating how agentic AI affects your network environment, DataEndure can help assess exposure and design controls that support both security and operational performance.