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The key to a well-functioning network is not costly bolt-on tools, but rigorous test and assurance that ensures the desired network state is not just achieved — but preserved at all times
The addition of AI capabilities to the network has provided telcos, hyperscalers, and enterprises with a very critical tool that can make or break the network. Now they must embrace continuous test and assurance to ensure their investment pays off.
AI offers a plethora of advantages when it comes to overseeing, regulation, and enhancement of the network infrastructure and procedures. It can analyze vast amounts of network data to surface patterns; it can ensure efficient distribution and utilization of resources; it can even anticipate potential issues and automatically deploy fixes — things network operators could only dream of in the past. But as with all disruptive technologies, there are some stumbling blocks.
Barriers to adoption
In the case of AI, there are multiple concerns. Starting out, there is a general lack of trust in AI’s decision-making abilities, and the low transparency and low explainability of AI tools only add fuel to the fire.
Security and privacy concerns have rose to the top in the recent years as AI’s potential to scrape private data has grown with time, and its many security risks have come to light through a myriad of security incidents.
“You are not just taking an existing application, taking the model we have and putting them together. Along with that, you are bringing in an entire AI ecosystem — AI infrastructure, tools, plugins, databases — all these various parts of the AI ecosystem talk to each other, they talk to the outside world. That increases the attack surface, adding new risks,” said Anand Oswal, senior VP and general manager, Network Security at Palo Alto Networks in a Packet Pushers podcast.
The changing regulatory environment places an additional burden on businesses, as changing regulations make it tricky to continually maintain compliance.
Role of Test and assurance
Getting around these hurdles requires knowing whether the AI capabilities added to the network are effective and trustworthy, and that knowledge comes through test and assurance.
Network test and assurance is not just about uptime — it is a systemic approach to validate configurations, services, and operational behaviors to ensure good network health from Day0 to Day1 and beyond.
Test and assurance is essential to maintaining network integrity and ensuring that the fabric performs within the pre-defined parameters of the organization. Vendors like Spirent, Cisco, Amdocs, NetScout, and many others have been offering assurance solutions for active and continuous network monitoring for years.
Continuous assurance, from device and RAN emulation to core and service observability, provide operational resiliency, and lay the foundation for autonomous operations. It involves testing various underlying components of the network fabric, as well as continually evaluating the AI tools and capabilities being layered on top of it.
Testing AI in the network
Testing AI solutions in the network entails validating AI’s efficacy and bringing transparency in their operations, which in turn builds trust and confidence in AI systems. Continuous governance provided through test feedback loops allows operators to continually improve and reinforce the models’ behaviors and capabilities.
Testing the network for AI
The flip side of the coin is of course testing the network itself, its infrastructure, functions and protocols, validating its performance, and ensuring compliance.
One of the many benefits of continuous testing and assurance is that it can allow operators to validate changes pushed by AI in real-time. Real-time validation prevents network changes from causing service interruptions or opening new vulnerabilities in the fabric that can become a security risk later on. Stress testing the network further reveals hidden vulnerabilities and weaknesses that attackers could potentially exploit. All of this happens proactively, meaning issues that can arise from pushing an update or adding a new capability are anticipated and dealt with before they even occur.
Enforcing test and assurance throughout the lifecycle massively benefits business outcome as well. It leads to faster time to market as new products and services can be auto-tested and deployed swiftly; lab through live automated testing cuts cost and complexity, delivering an even and optimum experience across users, while security testing allows operators to harden the attack surface, blocking bad actors from finding their way into the network.
With networks changing rapidly, moving from legacy physical infrastructure to the cloud and expanding at the edge, carriers are now pushing for continuous integration, deployment, and testing processes that build testing right into development, making it fundamental to network operations. Considering AI’s ever-expanding role in the context, it makes perfect sense, as test and assurance is integral to guaranteeing performance for not just the network but also it’s add-on AI capabilities.
