As the wave of digitization sweeps more enterprises with each passing day, the threats posed to organizations, particularly IT organizations, have never been more complex and pressing. With cybercrime statistics, including those for more targeted crimes (such as phishing) pointing towards an alarming frequency of attacks, the stakes for securing enterprises have been higher.
However, many IT organizations have been unable to smoothly transition into pushing the digitization of their organizations forward. This inability to support and drive digital transformation isn’t a result of a lack of effort on behalf of these organizations. Instead, it has everything to do with the unavailability of the proper management tools needed.
Unfortunately, many IT organizations lack the tactics and tools needed to overlook the smooth shift to digitization on such a grand scale, which often leads to the degradation of critical business applications, along with IT slowdowns and outages. Luckily, however, artificial intelligence for IT operations (AIOps) arrives like a beacon of hope for revolutionizing IT application, along with increasing business agility.
The Failure of Enterprise in Tackling the Burgeoning Complexity of IT Infrastructure
A primary reason behind the ever-present issue of IT slowdowns in enterprises and organizations that is frequently overlooked is the fact that the customer-facing side of IT (applications) is growing increasingly complex with each passing day.
As the increasing number of applications continue to occupy more and more space, the IT infrastructure grows into a vast and hybrid structure which often spans across multiple clouds on a grand scale.
Although the vastness of the IT infrastructure is a byproduct of the digitization drive taking enterprises by storm, it is still worlds away from the security infrastructure of yesterday, which was much easier to manage and analyze.
From the Internet of Things (IoT) connecting thousands of devices onto an organization’s network to complex issues such as bottlenecks, application slowdowns and outages, along with performance latency and the ever-present threat of phishing attacks via a free host website (000webhostapp, being the most commonly used host), organizations are just setting their systems up for failure at some time in the foreseeable future.
Owing to the flourishing complexity of the IT infrastructure, an organization’s system fails to cope with new, dynamic workloads for any multitude of reasons. Usually, the system is utilizing extra resources or is under the influence of a recent addition to the system.
In instances of outages or IT slowdowns, the pressure to fix the IT infrastructure falls on the IT team, who are tasked with the immense responsibility of analyzing and diagnosing the issues as they occur in real-time, along with coming up with a solution that satisfies customers and prioritizes the business aspect of an organization as well.
Unfortunately, however, without access to the proper tools and techniques, the IT team can only conjure security patches that resolve the problem for the moment but fail to withhold security in the long run. Also, without the ability to effectively predict a pattern for the ever-present security threats, the IT team is operating on a whim rather than basing their solutions on the rationale.
How Can AIOps Help the Situation?
To leave the momentous challenge of perfecting an organization’s IT infrastructure solely on the IT team is not only outdated, but it is also impractical. Taking into consideration the complexity of the threats facing enterprises, a solution that arches above each aspect of cybersecurity in an organization should be implemented.
Today, that solution arises in the shape of AIOps. The hybrid IT infrastructure has become too complex to be handled through traditional tools and tactics, which is why AI, along with machine learning and automation, has to step in to bridge the gap.
Unlike other security strategies, AIOps integrates a wholly modern and unique approach, which allows for the AI to view systems in terms of business outcomes. Simply put, rather than analyzing and diagnosing identities, infrastructure and applications independently, the AIOps platform follows the path taken by the user into the complex IT infrastructure and back out of it.
Ideally, AIOps platforms run automated investigations that derive the root cause of existing and recent issues on a regular basis. When it comes to managing a business-critical application, an AIOps platform can detect an unusual response within the system, whether it’s an anomaly in a workload profile or variant response to critical workload.
Other benefits to incorporating AIOps as a security tool include tapping into the truly unique set of capabilities offered, including predictive capacity management and forecasting/predictive abilities. The predictive ability of AIOps allows for the AI to smartly notify IT teams about any attack or potential outage, or slow down, before they actually happen so they may act accordingly.
By taking a different approach to the pressing issues faced by enterprises today, AIOps manages to do what several security strategies have failed to accomplish—provide organizations with an end-to-end, app-centric approach to security, which also takes into consideration the context at hand.
Moreover, AIOps will push organizations to shift their focus from reactive problem solving and cause them to optimize their IT infrastructure on a more proactive basis. Fundamentally, AIOps provides IT teams with the necessary tools to diagnose and combat both internal and external threats to an organization’s security infrastructure.
After the integration of AIOps within an organization’s infrastructure, enterprises can turn their attention into optimizing the balance of critical workloads, along with prioritizing business functions and improving applications, rather than worrying about security.
What Hurdles Can Enterprises Expect with AIOps?
Despite the countless benefits AIOps brings to the table, certain obstacles arise when it comes to the implementation of the security model into organizations.
Perhaps the most apparent obstruction organizations have to deal with is the extensive amount of time investment AIOps requires. Like every other security tool centered around an AI or machine learning technology, time is absolutely pivotal for the learning to actually take place. A simple way to smoothen the transition, AIOps must be fed with data, statistics and analytics originating from multiple data centers.
Another common issue that arises, mainly because of a lack of research, is when an AIOps platform fails to perform as per the specific requirements of an enterprise. Keeping this in mind, CIOs must voice their concerns about fine-tuning AIOps before committing to a vendor.
AIOps abilities in performing an in-depth analysis into the IT and security infrastructure—within the context of the applications it performs—gives the unique chance to stay ahead of the threats they face.
