Effective network management is critical for ensuring reliable system performance and safeguarding the flow of information that powers nearly every business operation. AI has quickly become the backbone of enterprise network management, setting the baseline for what secure and optimized networks will look like going forward.
From predictive analytics and self-healing architectures to AI-driven simulations that anticipate failures, organizations are using artificial intelligence to make their networks faster, safer and more resilient. Below, members of Forbes Technology Council share how AI is being used today to transform network reliability, performance and defense in ways that are poised to become standard practice across industries.
“AI will maintain a living identity digital twin, modeled as a knowledge graph of people, accounts, entitlements and systems. It will detect drift, orphaned access and toxic combinations; prioritize fixes; auto-generate audit evidence; and trigger revokes through identity access governance. Continuous, predictive identity assurance will become standard across hybrid estates.” – Craig Davies, Gathid
1. Analyzing Daily Network Datasets
Network operators gather massive datasets every day. Predictive and generative AI are key for analyzing that data, anticipating performance changes and proactively identifying threats and network issues, while maintaining customer experience. AI-driven models let operators simulate network scenarios, from environmental disruptions to regulatory shifts, enabling rapid adaptation and minimal downtime. – Fletcher Keister, GTT Communications, Inc.
2. Simulating Attacks With GenAI
Generative AI is changing the game for attack simulations. Instead of relying on static tests, it can mimic real hackers, evolving its tactics to find hidden vulnerabilities. This helps teams spot weaknesses before attackers do and fine-tune their defenses—something I think will quickly become standard for securing enterprise networks. – Pavan Emani, Truist Bank
3. Spotting Unusual User Behavior
AI is becoming essential for securing enterprise networks by detecting malicious behavior targeting files and unstructured data. Instead of relying solely on signatures or static rules, AI models continuously learn user and system patterns to spot anomalies like unusual access spikes or hidden exfiltration attempts. This proactive defense will be standard for safeguarding business-critical data. – Nick Burling, Nasuni
4. Enhancing Risk Assessment And Prevention
AI is optimizing enterprise networks in insurance by using predictive analytics for risk assessment and fraud detection. AI identifies patterns that indicate potential risks or fraud. This approach enhances security and streamlines operations, reducing the need for manual oversight. As insurers adopt AI, this practice will become standard, fostering greater trust and efficiency in operations. – Srinath Chandramohan, EY
5. Integrating Digital Identity Into Data Streams
One way AI is securing enterprise networks is by integrating digital identity into real-time data streams. This ensures that only authorized users and AI agents can access sensitive data, especially in multicloud and edge computing environments. It’s a simple, yet powerful, way to stay secure and compliant, and it’s something I believe will quickly become a standard practice. – Guillaume Aymé, Lenses.io
6. Governing Shadow AI and Data Leaks
AI-powered anomaly detection will become standard for enterprise networks. By learning normal traffic, AI can surface shadow AI use, data leaks and compliance gaps in real time, turning employee-led adoption from a liability into a secure, governed productivity driver. – Douglas Murray, Auvik
7. Continuously Monitoring Network Telemetry
In my work with predictive analytics and cloud transformation, we’ve implemented AI models that continuously monitor network traffic, user behavior and system telemetry to detect subtle deviations that human teams or traditional tools might miss. Over time, I am sure this will become as standard as firewalls or intrusion detection systems, with AI acting as a real-time guardian of enterprise networks. – Diganta Sengupta, Oracle Corp.
8. Enabling Self-Healing Networks
AI in enterprise networks will move from detection to self-healing autonomy. Beyond spotting anomalies, AI will predict failures, automatically reroute traffic and patch vulnerabilities before humans intervene. This shift from reactive defense to proactive resilience will soon define secure, future-ready enterprises. – Anusha Nerella
9. Preventing Data Leaks
AI will become standard for preventing data leaks in enterprise networks. Beyond detecting personally identifiable information or sensitive exposures, AI can flag when irrelevant or out-of-context data reaches a consumer endpoint. By ensuring only the right people access the right data, AI strengthens security, reduces risk and builds trust. – Subasini Periyakaruppan, Biotechnology Innovation Organization
10. Optimizing Network Traffic With Autonomous Routing
AI-powered network traffic optimization—where AI autonomously adjusts routing and bandwidth allocation based on live usage patterns—will soon be standard. This not only reduces congestion and improves user experience, but also boosts resilience against outages and attacks through intelligent, self-tuning networks. – Pradeep Kumar Muthukamatchi, Microsoft
11. Maintaining A Living Identity Digital Twin
AI will maintain a living identity digital twin, modeled as a knowledge graph of people, accounts, entitlements and systems. It will detect drift, orphaned access and toxic combinations; prioritize fixes; auto-generate audit evidence; and trigger revokes through identity access governance. Continuous, predictive identity assurance will become standard across hybrid estates. – Craig Davies, Gathid
12. Anticipating Network Disruptions
Predictive network optimization will become standard. AI analyzes network data to anticipate and prevent congestion or security threats, shifting management from reactive to proactive. For example, an AI system could analyze the acoustic profile of a server room to detect the unusual hum of a failing fan or the click of a compromised hard drive. – Harshal Shah
13. Implementing Zero-Trust Verification
AI-powered zero-trust network verification is becoming essential. Rather than just monitoring perimeter security, AI continuously validates every device and user interaction in real time. This proactive approach catches threats that traditional methods miss, but success requires thoughtful implementation with human oversight to avoid false positives that disrupt business operations. – Joseph Ours, Centric Consulting
14. Using Tokenized Identity For Adaptive Access
One way AI is transforming enterprise network security is through tokenized identity and access management. AI continuously monitors behavior and anomalies tied to cryptographically tokenized credentials, enabling real-time automated countermeasures like isolating compromised nodes or revoking specific tokens. This adaptive granular approach is likely to become standard in corporate cybersecurity. – Charles Morey, MobilEyes Inc.
15. Conducting Continuous Penetration Testing
AI-driven penetration testing is emerging and will transform security from quarterly, manual exercises into continuous testing at each software or infrastructure change. By leveraging the probabilistic nature and variability of GenAI models, enterprises can efficiently simulate diverse attack vectors and strengthen their posture. – Elliott Cordo, Data Futures
16. Eliminating Storage Silos
Enterprise networks often utilize multiple storage solutions, divided between departments, which are incompatible or inaccessible, resulting in costly and complex data transmission. AI can autonomously integrate with legacy systems built from varying architectures to seamlessly consolidate data fragmented across multiple storage platforms. – Daniel Keller, InFlux Technologies Limited (FLUX)
