AIOps for Cloud-Scale SASE
As the world shifts from a centralized application consumption model to a decentralized cloud-first model, where a large and growing number of employees are working from home or working from anywhere, enterprise IT operations and security strategies must evolve as well.
Secure access service edge (SASE) has emerged as a key platform for distributed, cloud-first organizations to deliver simplicity, scalability, flexibility, low latency and pervasive security. In fact, according to Gartner, “by 2024, at least 40% of enterprises will have explicit strategies to adopt SASE, up from less than 1% at the end of 2018.”
However, IT teams are challenged to automate mundane tasks, identify network and security issues quickly, and improve user experience while using antiquated tools.
Integrating artificial intelligence of IT operations (AIOps) with SASE overcomes this hurdle. AIOps incorporates AI, big data, machine learning and other cutting-edge analytics solutions to automate and improve IT management, perform anomaly detection, implement automatic security incident remediation and much more to significantly enhance IT operational efficiency.
Leveling Up Network Performance and Security with SASE
In networking, there are tradeoffs between security and usability. For work from home (WFH) users, the most usable system probably isn’t the most secure and the most secure system probably isn’t the most usable. So, how do IT teams provide security while making the system usable with low latency and high performance?
SASE combines software-defined WAN (SD-WAN) and distributed cloud performance into a single platform that delivers secure network access from anywhere under a single policy umbrella. SASE, being cloud-native, resides near the end user’s applications and provides centralized security while enabling low latency and high performance.
What’s the secret sauce that powers SASE? Approaches can vary, but a truly scalable approach delivers a global network of distributed cloud-based points of presence (POPs) that accomplish the same mission as a typical on-premises enterprise security stack, delivering a 360-degree view of the company’s network and any security issues.
SD-WAN is a fundamental component of SASE and improves WAN performance by creating an overlay network across multiple links and optimizing connectivity using edge devices, cloud gateways and a single-pane-of-glass orchestrator which IT teams use to apply security policy and controls for each user.
Gateways reside within POPs and are distributed globally in data centers, service provider networks and even directly in cloud provider networks. By intelligently connecting users to these gateways, IT teams optimize network traffic to maximize application performance and boost security.
WFH users leverage the overlay network to reliably, securely, and efficiently send their traffic to localized POPs instead of sending it on a lengthy trip back to headquarters for security and policy inspection, which would create significant latency.
For IT teams, SASE slashes their infrastructure workload, eliminating the need to maintain firewalls, cloud web security, VPN concentrators and other logistics, making centralized security management much simpler by using a central cloud-native service.
Once an IT team sets up the network, launches SASE and countless employees, devices and apps start using it, what problems are introduced and how does the system evolve in response? That’s the magic that AIOps delivers.
Scaling SASE with AIOps
What’s the root cause of most app issues? Most WFH users blame their home Wi-Fi or cable provider, while most enterprise-based users criticize the application itself.
It starts by understanding the application.
Applications are the reason we have networks, yet for too long, IT teams only monitored the infrastructure and assumed a sound infrastructure translated to quality application performance.
As users migrate from using on-premises apps to cloud-based apps, the burden of monitoring and troubleshooting multiple connection points lies outside the capabilities of legacy tools and the charter of networking teams. For example, a single large enterprise may yield tens of billions of data points per day and spanning thousands of network locations across countless users, which requires scale.
Scaling this measurement across all these users is no easy task, even for modern big data platforms and machine learning systems, due to the sheer number of data points that must be analyzed.
To effectively manage the path between user and applications and dependably deliver SASE at scale, IT teams apply AIOps. This helps them understand what’s really happening—ranging from performance issues to security vulnerabilities—by measuring network quality and each user’s end-to-end application experience. In fact, 90% of IT professionals believe that leveraging AIOps for network management will improve business outcomes for their organizations.
Harnessing AIOps’ Omniscience: A Superpower for IT Teams
By instrumenting and collecting performance statistics along the end users’ path—from work location to the cloud application—IT teams use AIOps to model the performance of every user and formulate a performance baseline. As a result, abnormalities are identified when a user’s performance deviates below the baseline.
Armed with a complete picture of every user and every application, AIOps correlates current network performance to detect deviations, reports why an issue occurred and suggests corrective actions.
How is this possible? By mapping every single end client device in the network and learning how it experiences the network from both a security and performance perspective, AIOps determines the optimum links at any given moment. Additionally, it measures any changes made to the network and assesses the efficacy of the changes.
Do deviations signal performance problems? Not always. But by analyzing all the users, AIOps separates real security and app issues from minor hiccups.
What’s the endgame? Using this knowledge, IT teams can use AIOps to self-heal and optimize user experience across the SASE solution. And because user performance is baselined and modeled, the organization fully understands device performance.
As more users WFH and more apps are introduced in the near term, only by harnessing the power of AIOps will IT teams effectively manage the challenges of delivering SASE at scale. This enables teams to collect and analyze volumes of data to improve app performance while also performing self-healing, where the network automatically finds data abnormalities or other risk factors and then employs programs to optimize itself.
