An interesting aspect of the AI revolution is that artificial intelligence is being used to solve the problems that AI creates in the first place. One use case helps iron out the kinks of another. For example, AI needs high bandwidth and low latency connectivity to process mountains of data. So, adopting AI in many networking use cases optimizes operations.
Autonomous Digital Experience Management (ADEM) tools provide real-time feedback on network speed and customer experience, proactively pointing out potential issues using predictive modeling. AI agents trained on network data can help network teams troubleshoot faster with natural language processing (NLP) controls and track down emerging cybersecurity threats.
Analysts predict that the market share of AI networking will grow from $15 billion to over $192 billion in the next ten years, a 32% year-over-year growth. We have just begun to explore the potential of AI in networking.
However, despite the benefits of adopting AI-powered networking tools, there are significant risks and challenges. Rapid adoption pressure can cause mistakes, such as security gaps. Shadow AI, in which employees use unapproved AI tools to automate aspects of their jobs, can expose proprietary data to a public LLM. All the while, AI basics such as ensuring accuracy and minimizing bias continue to be issues, especially for AI-immature organizations.
This post will explore these risks and challenges and the best practices to mitigate them.
Benefits of AI-powered networking
Integrating AI into networking has revolutionized how organizations manage and optimize their network performance, offering numerous advantages that enhance efficiency and security.
- Speed: AI can help route web traffic, and predictive modeling flags potential issues before they affect end users.
- Natural language troubleshooting: Networking professionals can query an AI network agent with NLP prompts to quickly identify trouble spots within a network. Machine learning algorithms “remember” issues and proactively suggest fixes in the future.
- Enhanced cyber threat detection: AI can help identify anomalous behavior that indicates malware.
- Boosted IT efficiency: Networks are increasingly complex. AI can reduce reliance on manual processes and help organizations overcome the IT skills gap.
- Visibility: Gain visibility into traditionally hard-to-manage devices, users, and traffic.
Learn more: AI powers the next generation of networking
Challenges of implementation
Enterprise organizations are like ocean liner vessels, taking time to change course. However, the current market pressures organizations to adopt generative AI in various use cases, often before the organization has a set AI strategy or supporting policies. A recent study conducted by Juniper Networks found that 87% of participants reported feeling pressured to adopt AI technology. The IT skills gap looms large in this situation, as few internal IT teams have the required experience to implement this emerging technology securely.
Additionally, enterprise networks are increasingly complex, with hybrid and remote employees becoming the norm and IoT devices increasingly common. Unless properly managed, these points of presence on the network can create backdoors for threat actors. Any AI network tools an organization uses must securely direct, route, and manage these users and devices while maintaining privacy and compliance mandates, especially in highly regulated industries like healthcare.
Key risks of AI in networking
Accuracy: Hallucinations are the primary antagonists for users who rely on AI to do their jobs effectively. Another concern is inaccurate or biased data from faulty training models that can inadvertently poison the AI data or model. The Juniper Networks study reveals that 87% of participants believe it might be impossible to determine the accuracy of their company’s AI-generated results.
Elevated threat actor activity: Bad actors are finding new ways to use or manipulate AI to enhance and exponentially increase the volume of their attacks.
Privacy: Protecting proprietary data or sensitive customer information is another key concern when deploying AI at scale.
Training and culture management: Implementing AI poses a perceived risk of job loss. Some employees may not be willing to learn or engage with AI tools. It is urgent to set the tone for AI implementation from IT leadership down, with regular training.
How to overcome the risks and challenges of AI in networking
To optimize the capabilities of AI in networking, prioritize hiring skilled personnel, expanding existing AI tools, enhancing model training, effectively educating employees, and establishing robust governance policies.
When considering generative AI solutions that rely on scraped data, examining potential biases and ensuring a thorough understanding of data consumption, model testing, and user interaction is crucial. Techniques like retrieval augmented generation (RAG) and human-in-the-loop solutions can significantly improve accuracy and reliability. Fortunately, AI network tools sidestep many accuracy concerns of other AI models because they train directly on network data, which can be cross-checked and confirmed in real time.
Establish clear policies regarding the use of AI tools, outlining both allowed and disallowed software. Communicate these policies effectively to your staff, ensuring they understand the restrictions, particularly regarding sharing confidential information with a public LLM.
Adopt a defense-in-depth strategy to mitigate risks associated with AI-generated vulnerabilities. Implement measures such as multi-factor authentication to reduce the likelihood of credential misuse and maintain strong network security, segmentation, monitoring, and access controls.
Regular penetration tests are essential to discover and address additional vulnerabilities in your environment before attackers can exploit them.
Learn more: AI in secure cloud networking: Current and emerging use cases
How CBTS can help you in your AI journey
CBTS collaborates with top networking industry leaders to deliver exceptional tools and solutions expertly integrated into your enterprise network. We empower you with a comprehensive view of your entire network, allowing you to enhance performance, bolster security, and elevate the overall user experience.
We understand that having a unified perspective on your network operations is crucial in today’s fast-paced digital landscape. Our solutions provide visibility through a single pane of glass, enabling you to identify issues proactively, optimize resource allocation, and streamline processes for maximum efficiency.
As you embark on your AI journey, we invite you to reach out and discover how our tailored services can best support your organization’s unique needs. Let us help you harness the power of AI to drive innovation and growth within your enterprise. Together, we can navigate the complexities of modern networking to achieve your strategic objectives.
