With the rapid innovation and evolution across the AI, ML, and automation industries in the last five years, we’ve seen an equally rapid shift in how industries approach software and system development. This is especially true for network management, which has traditionally required rule-based configurations, human intervention, and other methods that can result in higher error rates and slower development cycles. This more manual approach may also lead to software that is unintentionally more vulnerable to ever-advancing cyberattacks and other new threats.
How AI is Helping Network Management System Development
Thankfully, AI can cover some of these gaps and improve efficiency for more resilient and more capable network management systems. For example, GenAI techniques can be leveraged to identify inactive or latent malware on a network based on learning behavioral patterns (even seemingly benign ones), before locating and eliminating the threat. While this could be performed manually, AI’s ability to scale these processes is unparalleled. Software that involved AI-powered development, testing, and prevention measures might also dynamically re-optimize and re-route the network to adapt to the failure of nodes or localized losses of connectivity.
AI-driven code generation and optimization are other ways that network management is being revolutionized. LLM tools enable developers to write efficient, error-free code more quickly, accelerating feature delivery for SDN controllers, network security, observability tools, and more. Once code is created, AI has also been overhauling the testing process to catch more bugs (and even predict bugs) in edge cases that wouldn’t have been caught by manual testing.
AI-powered test management platforms are already using image and character recognition to automatically update test cases so they don’t break when UI elements are changed. This self-healing or self-optimization ability isn’t limited to testing and development, though. Once a network management solution is live, it still requires ongoing lifecycle management with bug fixes, updates, security patches, and performance upkeep. An example of this might be a platform that uses AI to predict system failures and autonomously patches vulnerabilities or optimizes firmware to improve efficiency and avoid outages.
While this can help with unintentional outages, malicious outages are another important area for network management to monitor. With cyberattacks on the rise, it’s critical to incorporate more efficient and scalable tools that proactively identify weak points in software before they’re exploited. This goes beyond simply checking for common vulnerabilities (but faster), and moves into more comprehensive fuzzing tools to continuously test invalid inputs and find possible weaknesses. It could also involve behavioral analysis to detect anomalous patterns that might indicate a security threat, or even simulating cyberattacks to gauge the effectiveness of existing defenses. All of these help streamline and bolster network management development for more advanced, resilient, and agile solutions.
Future of AI in Network Management & Security
As this technology continues to evolve and improve, it will get better at threat detection to identify and locate potential concerns before they turn into problems. This allows teams to catch threats before they snowball out of control and minimizes interruptions for users. We also expect to see more effective and proactive self-healing systems support manual efforts. This includes detecting, creating, and even deploying security fixes “on the fly” without the need for human intervention. However, this more action-oriented purpose requires a slightly different type of AI than the common generative AI models we currently see everywhere.
On that note, we’ve seen huge growth around “Agentic AI,” or using AI to actually perform tasks rather than simply generating content (code, text, images, etc.). This allows AI engines to use “chain of thought” processes to perform reasoning in real-time, which is especially useful for automating workflows or tasks that aren’t the same as the last time you ran them. We expect AI Agents (as tools like ChatGPT, Gemini, and Perplexity have been rolling this functionality out to the public) to replace existing solutions like RPA.
AI Agents will become more integrated with the software development lifecycle, assisting in the automation of processes like compliance enforcement and security policy generation. The efficiency of these algorithms will also enable more strict security policies, without necessarily affecting all users. By evaluating behavior data, AI might enforce certain policies dynamically based on the potential risks posed.
Challenges of AI-Driven Software Development for Network Management
Network management still involves many legacy systems and frameworks, which aren’t always compatible with new AI techniques and tools. Overcoming this hurdle and investing resources into effectively integrating these two areas will determine whether AI improves or hinders your processes.
As with any AI training, unbiased and representative data is critical to success — a lack of this high-quality data may lead to unreliable recommendations, updates, and more. Even with perfect training data, GenAI is still subject to hallucinations that could give false positives or negatives, hampering its effectiveness. Ironing out these issues (potentially by using multiple models) will be critical to ensuring accurate and reliable AI systems.
Earlier, we mentioned the importance of cybersecurity regulations, and this includes AI-driven decisions as well. For example, documentation of changes or decisions must be explainable to comply with security and data privacy regulations, which can still be a challenge for some AI. Cybersecurity concerns could also be further exacerbated by poorly designed Agentic AI — if a bad actor compromises these, it could perform nefarious actions or tasks while still authorized.
Conclusion
There is a huge amount of potential when it comes to AI’s impact on network management, but it needs to be approached in a thoughtful way that mitigates the challenges outlined above. AI is already an incredibly helpful tool to help automate tedious processes like security testing and accelerate ongoing lifecycle management like patch deployment. However, it still must be balanced with human verification and guidance to maximize value while minimizing risks. It’s not an understatement to say that this is a critical inflection point for the network management industry. To stay ahead, embracing AI-driven development is more than an advantage, it’s a strategic necessity. AI-driven software development is no longer just an optimization tool — it’s becoming a requirement for building resilient, secure, and self-adaptive network management solutions.
