AIOps is an approach to managing the exponential growth of IT operations and the complexity of new technology through the application of artificial intelligence (AI). IT infrastructure increasingly relies on complicated deployments, multi-cloud architectures, and huge amounts of data. Traditionally, the tech industry responds to complexity by applying extra brainpower to the problem, bringing in more engineers, developers, and management. But as the demands on software teams continue to grow, it has become apparent that adding more staff is not a scalable solution. Instead of human intelligence solving these problems, the IT industry has turned to artificial intelligence solutions to help teams deal with software’s incredible scale and endless innovation.
Take cloud misconfiguration, for example. These events occur when a team establishes inadequate settings for cloud data protection. But these misconfigurations are not a product of insufficient brainpower. Instead, they are practically inevitable in a world of distributed computing and everything as a service (XaaS).
A sophisticated IT system may generate enough of these events to outstrip the maintenance capabilities of even our most intelligent and efficient teams. Each incident demands our time and attention, or we risk leaving our infrastructure vulnerable and our data — along with that of our customers — exposed to attackers.
Enter the world of AIOps.
A brief history of AIOps
Research firm Gartner first coined the term AIOps in 2016. Combining the phrases “artificial intelligence” and “IT operations,” Gartner gave a name to a developing force in the IT industry, defining AIOps as the application of AI to IT operations.
AIOps uses machine learning and big data to assist IT operations. In this sense, the practice almost certainly predates Gartner’s definition of the term — companies at the forefront of AI research have likely been applying algorithmic processes to assist their internal operations for years.
What was once a rarity in the field is quickly becoming a requisite in the modern IT environment. Systems have multiplied their components in the industry-wide shift to containers and microservices. Developers working in the convoluted IT space have become more and more insistent about wanting the assistance of AI tools.
It might be easy to dismiss AIOps as yet another passing trend in a market flooded with AI-powered software as companies seek ways to market their machine learning tools. However, the security challenges introduced by big data and software at scale are tangible. Without some form of AIOps, IT organizations are sure to find many obstacles stifling their growth.
Cooperating with AI
AIOps’ most significant contribution to software development is at the intersection between artificial and human intelligence.
Finding and diagnosing errors and inefficiencies is an ongoing challenge for any IT operation. Companies routinely build, deploy, and run software at a gigantic scale, servicing millions — even billions — of people. These colossal systems and their enormous userbases generate volumes of data that no organization could hope to sort through manually. The strength of machine learning is its ability to analyze and act on this monumental amount of data.
Effective AIOps acts as a frontline interpreter for all this data. AIOps enables organizations to make the most of their development time by monitoring logs, filtering information, and using machine learning to find patterns and insights. With help from AIOps, teams can off-load time spent on tedious tasks like poring over logs and trying to isolate the source of issues across complex systems.
Detecting and diagnosing bugs and bottlenecks is an ongoing challenge for any IT operation. AIOps sifts through raw data to help developers rapidly address issues identified by their AI tools. AI can notify teams about unusual activity, provide alerts for errors, and act as an early warning system to spot problems before they become critical. Not only does this help with mediating internal bugs, but it also helps identify and circumvent malicious external attacks.
Humans are ill-equipped to deal with the scale of modern software. AIOps saves time and hassle by performing a security function that human intelligence is poorly suited to handle.
DevOps vs AIOps
Anyone with a career in IT might observe the development of AIOps with apprehension. However, AIOps is not here to replace DevOps or even compete with it. The primary application of AIOps is monitoring and alerting — a decidedly complementary role to existing IT departments.
DevOps teams constantly deal with data from disparate sources, handling data from any number of cloud platforms, Kubernetes clusters, serverless functions, and everything else in their deployment. All that information must be collated and correlated with important variables. It is a challenge that can benefit from a bit of automation.
Your typical AIOps is configured as a digital guard dog, alerting traditional DevOps teams to errors and anomalies. More complex AIOps can take on an analytical role, crunching the numbers on essential metrics to provide suggestions for improving software or optimizing deployments.
While unlikely to supplant DevOps any time soon, AIOps is undoubtedly a harbinger of things to come. DevOps practitioners need to adapt to environments with increasing automation and find ways to integrate machine learning and other AI tools into their workflow. Without a willingness to embrace these new tools, the software industry’s ability to scale will stagnate, and potential threats to application security will emerge.
How to incorporate AIOps into your DevOps practice
Most organizations first venture into AIOps with a monitoring role. AI continuously scans systems and flags any potential problems. This style of ongoing anomaly detection can be beneficial for preemptively mitigating outages and spotting warning signs before they develop into catastrophes.
AIOps monitoring can act as a security layer by keeping a watchful eye on your entire system. Its ability to process vast volumes of information in milliseconds allows it to preemptively identify security flaws or detect patterns that might indicate a security breach. You can further integrate the security potential of AIOps with other crucial tools available for securing large-scale applications to ensure our data is safe behind multiple layers of redundancy.
After using AIOps for monitoring and security, it is a natural progression for most teams to integrate AIOps tools into their DevOps strategy. Beyond providing immediate benefits, like automating repetitive tasks and making crucial data more readily available, AIOps systems can also act on the conclusions they draw.
Organizations can also adopt AIOps for diagnostics. Used as a diagnostic tool, AIOps is a powerful assistant for root cause analysis. When failures inevitably happen, AIOps plays an active role in alerting teams to the problem and directing them toward its source. As AIOps has grown more capable, it has come to play a role in remediating issues.
By continuously analyzing and suggesting precise adjustments for metrics like resource usage, machine learning algorithms can also fine-tune system performance and identify new opportunities to harden security or optimize architectures and deployments.
Embracing the future of AIOps
Integrating AIOps tools into a DevOps strategy is a logical evolution for most teams. DevOps programs benefit from off-loading repetitive tasks and instead devoting efforts to improving software. AIOps also provides teams with better access to information about their software’s performance to drive these improvements.
Rather than a threat, we see AIOps as a superpower that enables DevOps teams to manage more (and more complex) datasets than humans have the ability to. The right AIOps strategy can help DevOps teams conquer the enormous, inter-connected set challenges inherent to scaling software. Large organizations are empowered to scale with minimal growing pains, while small teams can avoid bloat by leveraging AI instead of adding more staff. And, as its capabilities expand, AI is likely to become even more important to DevOps and IT. Automation will also become more intelligent and more active in remediating issues and providing fine-tuning recommendations.
If you are a DevOps practitioner hoping to future-proof your team’s strategies, AIOps should be one of the approaches you investigate. Embracing automation and integrating AI and machine learning tools into your workflows will give you the opportunity to scale your enterprise safely and securely.
Get the eBook 6 Paths to Application Security to learn more about how you can implement AIOps into your development process and how AI can help you maintain application security.