AI applications are embedded in our phones and becoming a vital part of life. To accelerate mainstream adoption, technology companies are inundating us with TV commercials to show the magic of AI. “Summarize a research report.” “Make this email sound professional.”
Many people don’t realize that as they watch these commercials and experiment with the technology, most of these capabilities are based on language, particularly large language models (LLM). On the consumer side, breakthroughs in natural language processing and improving search engines are great. Andrej Karpathy, Open AI co-founder, referred to this when he said, “The hottest new programming language is English.” But this is not necessarily where the real power of AI is for enterprises.
Although nearly half (49%) of CEOs use AI for content generation, communication, and information synthesis, implementation more broadly across enterprises is flat or cooling. Enthusiasm for AI to enhance productivity, reduce downtime, and increase ROI is there, but the full potential is untapped due to cost and security concerns.
Initial AI applications have relied heavily on machine learning (ML), a subset of AI that has evolved into transformer architecture or look-ahead architecture. ML models basically predict what the next word, the next sentence, the next paragraph will be, and so on. However, training a model costs millions of dollars before it adds value and must be done responsibly. Using flawed or biased data can lead to inaccurate results. You must also lasso the data and the systems it connects to so that sensitive data isn’t exposed.
This is where the newest innovation in AI, distinct from ML, is coming into play to enable additional enterprise use cases. With the right boundaries, new AI can provide game-changing value, including assistance in building cyber resilience.
Delivering Cyber Resilience Insights
According to Gartner’s latest Hype Cycle for I&O Automation, by 2026, 50% of enterprises will use AI functions to automate Day 2 network operations, compared with fewer than 10% in 2023.
The new generation of AI will help us get there.
AI is now moving from training to inference, helping you quickly make sense of or create a plan from the information you have. This is made possible based on improvements to how AI understands massive amounts of semi-structured data. New AI can figure out the signal from the noise, a critical step in framing the cyber resilience problem.
The power of AI as a programming language combined with its ability to ingest semi-structured data opens up a new world of network operations use cases. AI becomes an intelligent helpline, using the criteria you feed it to provide guidance to troubleshoot, remediate, or resolve a network security or availability problem. You get a resolution in hours or days – not the weeks or months it would have taken to do it manually.
Enabling Better Network Automation
In the same study, Gartner also finds that by 2026, 30% of enterprises will automate more than half of their network activities – tripling their automation efforts from mid-2023.
AI is not the same as automation; instead, it enhances automation by significantly speeding up iteration, learning, and problem-solving processes. New AI allows you to understand the entire scope of a problem before you automate and then automate strategically. Instead of learning on the job – when you have a cyber resilience challenge, and the clock is ticking – you improve your chances of getting it right the first time. As the effectiveness of network automation increases, so too will its adoption.
Let’s look at the challenge of vulnerability management as an example.
Imagine you are a managed service provider (MSP). A flaw has been discovered in an open-source library that’s typically included in most of the popular switches made by multiple vendors. You, your customers, the vendors, and the bad guys all hear about this vulnerability at roughly the same time. Your job is to figure out how to remediate faster than the bad guys who will accelerate attacks because they know the door will close.
Today, you have to manually figure out what to do across a complex and distributed network environment consisting of different customers, switches, and versions of switches that may or may not be running a version of the library with this vulnerability.
You write one automation script after another to remediate each scenario. But you don’t see the commonalities until you’re well into the project. Eventually, you realize you could have written a handful of scripts to cover most of your customers, but by then, it’s too late.
New AI allows you to streamline the project by formulating an AI-based lookup. You can pull in customer configuration information automatically and then use AI to categorize customers based on that criteria to see how cyber resilient they are. AI can also provide recommendations on how many unique automation scripts you will need to write so you can focus your resources and build resilience faster.
The Magic of AI: Enabling Cyber Resilience
AI is never certain, but it can give you high-probability guidance, and that’s what business leaders look for to help them manage their enterprises strategically.
You can get to cyber resilience faster when AI can provide insights that help you slash the amount of prep work and time spent writing automations to solve network security and availability problems. For business leaders, that’s more than magic. That’s a compelling use case for AI.
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