Yet AI isn’t only useful to enhance efficiency and user experience. Its ability to intelligently analyse data in real time also makes it an excellent tool for network security.
How AI can enhance network security
AI-powered security solutions can monitor network operations for security issues and alert network engineers or automate incident responses.
Threat detection
Monitoring historical data and traffic data in real time, AI-powered systems can identify abnormalities or known patterns that may indicate a potential cyberattack. For example, it has the potential to detect zero-day attacks, which are usually missed by traditional signature-based detection methods.
Automated response
Once a potential threat is detected, AI-enabled risk analysis can triage and automate incident responses to prevent escalation, contain damage or enable rapid recovery. For instance, it can update firewalls, block malicious traffic or “clean” infected files.
Device tracking
AI can also help with one of the most demanding network security challenges – tracking connected devices. As IoT devices proliferate, machine learning can help identify, categorise and manage them, checking for potential vulnerabilities and outdated software.
Policy automation
Similarly, AI can create and deploy security policies as required. For example, it can allow or deny access to specific devices, users or apps, dynamically responding to changes on the network.
Whatever the security issue, AI has the potential to speed up human responses or deploy fast, automated self-healing, countering a potential threat before it escalates.
AIOps and the future of networking
Despite the enormous potential benefits, the AI-enabled solutions outlined above are yet to be widely implemented in the industry. So-called AIOps – artificial intelligence for IT operations – is still in its infancy.
However, as machine learning and other AI technologies evolve at breakneck speed, expect to see AI’s role switch from cameo to hero. From network design and deployment to maintenance and customer service, AI will become integral to future network operations.
One emerging trend is to apply AI to streamline network services. Machine learning can enhance zero-touch provisioning and enable end-to-end network automation.
Another is harnessing AI for software-defined networking (SDN). For instance, as more IoT devices come online daily, engineers can use AI-enhanced SDNs to design and control scalable, secure industrial IoT networks.
The challenge of AI in networking
Estimated at around $8.3 billion in 2022, the global AIOps platform market is expected to reach about $80 billion by 2032. Yet AI in networking faces several challenges before it becomes mainstream, such as:
- Complexity and integration: Today’s networks are increasingly complex with multiple interlinked components, data sources and interfaces. Integrating AI solutions requires rethinking pre-existing networks to allow for this complexity.
- Data integrity and security: AI relies on maintaining and processing vast quantities of high-quality data. Any emerging AI networking solutions must ensure data integrity, security and privacy by design.
- Interoperability and open standards: Data from multi-vendor networks remains challenging because the data format and syntax may differ by vendor. In future, the evolution of open standards, like Open RAN, should help meet this challenge.
- Skills gap and retraining: Integrating AI in networking will require a change in working culture for network engineers. As AI technologies evolve, they’ll need to learn new skills and working methods to deploy and maintain AI-powered networks.
In short, AI won’t transform networking overnight. A world of automated, software-defined, self-healing, self-defending networks is still some way off.
