Transforming network and security with AI and ML
David Odayar, Security and NGS Business Lead at Westcon-Comstor
Finding a way to overcome manually-intensive and complex IT environments that require access to information and resources is one of the critical challenges facing most organisations today. And when one considers the emergence of hybrid work, multi-cloud environments, and edge computing, it has become imperative to inject automation into processes to benefit from greater business flexibility.
This is where the adoption of Artificial Intelligence for IT Operations (AIOps) makes it possible for business and technology leaders to gain an understanding of the growing amount of data generated within IT operations. This information can be analysed to identify relevant performance patterns and be presented constructively to realise agile and predictive decision-making.
But to effectively use an AIOps model requires the business to integrate numerous existing IT platforms, tools, and processes, unifying access to information, insights, and capabilities previously managed in separate silos. However, with an AIOps environment in place, activities such as network remediation can be solved in minutes instead of hours.
Fortunately, more companies are adopting IT strategies that facilitate the transition to AI-enhanced ITOps and NetOps. Businesses from all industries can start reaping the benefits of optimised IT and network operations while enabling innovation through intelligent data consumption across the entire business. This is a great way to enhance the benefits of AIOps further. Still, the AIOps roadmap requires evaluating real-world use cases that defy traditional infrastructure approaches while generating a larger sense of urgency for adoption.
Removing obstacles
Critical to the success of AIOps platforms is to train algorithms. This entails a process of operationalising AI which requires massive amounts of data to flow unhindered through a complex network pipeline. As the business becomes more reliant on AI technologies, bottlenecks occur across the environment, from the edge to the cloud and back to the organisation.
Building a data fabric capable of unifying data management across devices, data centres, and multiple clouds will help ensure that AI data can be ingested, collected, stored, and protected no matter where it resides. Only once that happens can a business optimally train AI, drive ML, and empower the deep learning algorithms necessary to bring its AI projects to life.
To this end, AI solutions must be designed to remove these bottlenecks and enable more efficient data collection, accelerated AI workloads, and smoother cloud integration. Having access to a unified data management environment that supports seamless, cost-effective data movement across a hybrid, multicloud ecosystem is critical.
Securing everything
Of course, securing this data journey remains important, especially in today’s complex regulatory environment. Given how the volume and complexity of cyberattacks are increasing, the efforts to identify and contain cyber threats have moved beyond the human scale. Combining AI with cybersecurity will give security professionals additional resources to defend against these threats.
Just think of the benefits of automating mundane security tasks such as vulnerability management, antivirus, identity management, and mail hygiene. For example, Google increased email hygiene by employing AI to block an additional 100 million spam messages per day. And then, there is the opportunity to perform behaviour analysis of a vast number of signals to identify and block what could be construed by human operators as seemingly legitimate transactions generated by bots.
As cybercriminals start using AI technologies to launch more sophisticated attacks, companies must also employ AI and ML to safeguard their networks and data better.
Companies cannot rely only on traditional detection engines to defend themselves. Instead, they need technology that incorporates AI and provides them with a unified, multi-layered security architecture. By doing so, an organisation can detect threats and use AI and ML tools to prevent complex attacks.
Planning for unpredictability
Organisations’ increasing digital transformation efforts across industry sectors have invariably resulted in gaps emerging across their network and security real estate. Therefore, a common theme is how a company can manage the unpredictability of an environment where AI and ML introduce additional layers of complexity regardless of the benefits they bring.
Strengthening the defensive stance of organisations is the increasing adoption of next-generation firewalls that use ML to react to the last attack on systems. These also help prevent zero-day threats inline and automate policy recommendations. For its part, ML can also assist companies in their quest to prevent business-disrupting incidents caused by dynamic factors such as changes in network traffic patterns, system-related factors such as software defects, and configuration changes.
Benefitting from a channel approach
Linking all these vendor solutions into a cohesive whole is one of the value propositions resulting from an integrated and healthy channel environment. Giving resellers access to an experienced distributor who has the tools and skills to improve business processes for increased operational flexibility can become a significant business enabler.
It is as much about integrating the latest technologies into traditional approaches as it is about access to the latest solutions, training, and insights to deliver a competitive advantage.