Cloud environments, which often consist of dynamic multi-cloud environments, with containers and microservices have become complex.
It’s becoming increasingly difficult for IT teams to correlate system events with root causes when there are billions of dependencies between applications, clouds, infrastructure and microservices to keep track of. And that’s where AIOps come to the rescue. Recently, a rise in data breaches has underscored the need to deliver strong, embedded security with AIOps platforms.
AIOps is a wide category of tools and components that uses artificial Intelligence (AI) and analytics to automate common IT operational processes, detect and resolve problems, and prevent costly outages.
Merging AI and automation to IT Ops, AIOps automate manual configurations, streamline cloud environment complexity, provide quicker solutions to reducing alert noise and find root-cause answers, as machine-learning algorithms learn as they go how systems perform, and detect problems and anomalies.
In other words, AIOps builds a culture of trust around AI, alerting enterprises to real problems impacting end-users, and links them to root causes. And the more IT trusts AI to automate its functions, the more time the team can spend on innovating.
For organisations relying on legacy processes, but want to invest in automation, they might be exposed to their inefficiencies, particularly when it comes to security. Layering the traditional monitoring tools with machine learning (ML) algorithms makes it harder for IT. Even with the best ML model, the basic equation remains the same: bad data will bring in doubtful results.
For instance, monitoring tools might register all green lights, but the end-users could still have system problems. Stacking ML cut down the number of alerts because it’s interpreting false green lights as real. Traditional monitoring can’t keep up with dynamic enterprise environments. The manual efforts needed to tune, manage and configure the alerting rules for monitoring tools that ML can’t do is massively time-consuming.
To execute an AIOps solution that gets the best data, and therefore the best results is possible by deploying a deterministic AI system that can ingest data with relational context from different APIs across the full tech stack.
Unlike the traditional ML approach, deterministic AI doesn’t draw on data provided by different tools monitoring different layers of stack in isolation. Instead, it captures five distinct dimensions of data — topology, traces, metrics, logs and events — by connecting that data to context.
The level of context enables a deterministic AI approach to work fast and reliably in analysing data, pointing out what is impacted and the root cause of the issue, and then auto-trigger remediation actions.This feeds into creating an AIOps process that facilitates automated, high-fidelity data-backed solutions.
It’s not just about streamlining cloud complexity and providing faster, precise solutions but also changing the role that operations engineers play in IT.
AIOps frees up engineers to become engineers with developer skills, and use those skills to mentor their development teams, provide the business with a powerful platform for deploying and operating applications and services in a fully automated and self-service fashion and generate new products and new innovations faster than would otherwise be possible.
Bolster cybersecurity
Now, as adoption of AIOps platforms gains momentum, IT decision-makers are using the technology to bolster cybersecurity, in integration with other security tools, and guard against a multitude of threats.
The mounting complexity in organisations’ application environments, spanning public and private cloud deployments, and hybrid working amounts to an increase in the number of edge-computing devices, all which require protection.
When it comes to cybersecurity, speed matters the most — identifying the source location of a cyberattack and when it occurred. Since AIOps platforms use collected streaming network telemetry data, they can auto-discover, classify, and inventory devices. Besides, they can also reach for all wireless, wired, and IoT devices communicating in the cloud or on the corporate network.
AIOps platforms apply analytics and AI to the data to determine the typical behaviour of an organisation’s systems and if it finds anything suspicious that exceeds a threshold defined by AI, an alert is sent to security administrators detailing the threat and the steps they need to take to eliminate it.
AIOps can help organisations identify, isolate, and respond to security issues and determine, for example, whether a potential problem is ransomware. AIOps platforms have complete visibility into an organisation’s data, spanning traditional departmental silos, and which helps to ferret out threats with longer-term effects, such as leaking customer data and in turn, causing massive reputational damage.
Most AIOps tools integrate with security information and event management (SIEM) and security orchestration, network firewalls, automation, and response. These external security tools, combined with AIOps traffic behavioural analysis, can better monitor a wide range of threats.
But human involvement is required to use AIOps platforms for security — to tell the AI within AIOps which services, apps and resources are business-critical. Fine-tuning of these tools leads to an effective categorisation of network components for behaviour analysis.
Besides, recognising key data flows ensures the AIOps platforms understand what security events are more important than others and provide comprehensive information on the threat, its effect, and steps that should be done to contain it.
The global AIOps market is estimated to grow at a CAGR of 30 per cent over 2022-2030, driven by the advantages it offers over DevOps, such as higher accuracy and reduction of false positives.
Moreover, advantages of AIOps platforms, such as data output, analytics, aggregation, automation, and flexible and centralised multi-layered platform, are expected to boost its growth in coming years not just among big companies but the entire corporate ecosystem, from on-premises to the public, private, and hybrid clouds to the network edge, where resources and IT staff are scarce.
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