A review by network security engineer Naveed Uddin Mohammed and four co-authors maps the uses of machine learning in network operations while emphasizing the limits of autonomous control.

Modern enterprise networks produce more operational data than engineering teams can examine manually. Cloud services, remote users, Internet of Things devices and software-defined infrastructure generate continuous streams of traffic records, configuration changes, performance measurements and security alerts. Artificial intelligence can help interpret that volume, but deciding where automation is dependable remains an engineering problem.

That distinction is central to *Networking with AI: Optimizing Network Planning, Management, and Security through the Medium of Artificial Intelligence*, a June 2025 review paper by Naveed Uddin Mohammed and four co-authors, published in the *International Advanced Research Journal in Science, Engineering and Technology*. Mohammed works in enterprise network security and studies AI applications in network management and defense.

The paper surveys machine learning, deep learning, reinforcement learning and natural-language processing. It examines their potential roles in traffic management, security monitoring, fault recovery and quality-of-service optimization. Rather than reporting a new deployment or controlled experiment, it organizes existing research and industry examples to explain where AI may assist network operators and where risks remain.

From Static Rules to Adaptive Operations

Traditional network-management systems rely on predefined thresholds, signatures and operating rules. These controls remain necessary, but they can struggle when traffic changes quickly or one symptom has several possible causes.

Machine-learning systems can establish statistical baselines, classify traffic and flag deviations. Reinforcement-learning systems can evaluate routing or resource-allocation decisions under changing conditions. Deep-learning models may identify patterns across packet, flow, log and telemetry data, while natural-language tools can make operational records easier to query and summarize.

Mohammed and his co-authors present these techniques as complementary rather than interchangeable. A congestion-prediction model is not automatically suitable for intrusion detection, and a tool that summarizes logs should not be assumed to make reliable remediation decisions. The objective, available data and consequences of error determine which technique is appropriate.

This approach is consistent with the National Institute of Standards and Technology’s work on trustworthy intelligent networks. NIST studies AI and machine learning for DNS-abuse detection, botnet detection, anomalous routing activity, distributed-denial-of-service mitigation, and network management. It also emphasizes methods for measuring whether these systems remain robust.

Three Areas of Practical Use

The review highlights three practical applications. The first is traffic engineering: models can use historical and current measurements to predict congestion, recommend routes and allocate bandwidth. Google’s published work on its B4 software-defined wide-area network demonstrates how algorithmic traffic engineering can support large private networks.

The second is fault detection. Conventional monitoring often depends on device counters, logs and management interfaces, creating a blind spot when malfunctioning equipment reports that it is healthy. Meta’s NetNORAD uses end-to-end probes and packet-loss and latency measurements to detect failures that device polling can miss.

The third is security analytics. Machine learning can help rank alerts, identify unusual behavior and connect events from several data sources. It cannot, however, turn every anomaly into proof of an attack. Software releases, routing changes, new cloud services and legitimate increases in activity can all produce unusual traffic.

“The useful role for AI is to narrow the investigation,” Mohammed said. “The response still depends on understanding why the systems are communicating and what the operational effect of blocking that communication would be.”

Why Context Remains Difficult to Automate

Network decisions affect more than the event being analyzed. Blocking a connection may stop malicious traffic, but it may also interrupt authentication, payments, manufacturing operations or clinical access. Rerouting traffic may relieve congestion in one location while creating problems elsewhere.

AI-assisted operations should therefore be judged by their effect on the entire network, not only by model accuracy. False positives, delayed detection, model drift and incomplete telemetry can undermine a system that performed well during testing.

This is especially important for automated remediation. Engineers can review a recommendation alongside network topology, application ownership and recent changes. An automatically executed action may have no such checkpoint. Higher-impact actions require bounded permissions, rollback mechanisms, audit records and human approval.

The Risks Introduced by AI

AI adds another system that must be governed and secured. Models depend on data whose quality and origin may be difficult to verify. Attackers may manipulate inputs or evade learned patterns. Infrastructure changes can make historical baselines less representative, while centralized telemetry creates privacy, retention and access-control concerns.

The review identifies data privacy, interpretability, computational cost, integration and adversarial manipulation as recurring challenges. Production systems consequently need continuing evaluation rather than a one-time accuracy result. Teams must know what data informed a recommendation, monitor performance changes and recognize when a model operates outside its evaluated conditions.

A Framework, Not a Deployment Study

The paper’s contribution is primarily organizational: it brings AI techniques, network applications, industry examples and implementation risks into one framework. It does not validate a particular model on a live enterprise network, and its examples should not be treated as proof that results will transfer to every organization.

Enterprise networks differ in architecture, traffic, priorities and tolerance for disruption. Claims about autonomous or self-healing networks must therefore be tested under actual operating conditions.

AI can forecast demand, surface unusual behavior, correlate telemetry and recommend possible actions. Engineers must still validate those recommendations against topology, application dependencies, identity, policy and business impact. That combination—machine-speed analysis with explicit operational controls—will determine whether AI makes enterprise networks more resilient or simply adds another source of complexity.

 Sources for editorial verification

– “[Networking with AI: Optimizing Network Planning, Management, and Security through the Medium of Artificial Intelligence](https://doi.org/10.17148/IARJSET.2025.12649),” June 2025.

– NIST, “[Trustworthy Intelligent Networks](https://www.nist.gov/programs-projects/trustworthy-intelligent-networks).”

– Google Research, “[B4 and After](https://research.google/pubs/b4-and-after-managing-hierarchy-partitioning-and-asymmetry-for-availability-and-scale-in-googles-software-defined-wan/).”

– Meta Engineering, “[NetNORAD: Troubleshooting Networks via End-to-End Probing](https://engineering.fb.com/2016/02/18/core-infra/netnorad-troubleshooting-networks-via-end-to-end-probing/).