By Vishal Muktewar - April 22, 2022 3 Mins Read
The rapid surge in the number of cyber-attacks has exposed the vulnerabilities present in the infrastructure of the organization. One way for organizations to deal with them is to incorporate AIOps that provide them better visibility into performance and system data on a large scale.
Accelerating the digital transformation initiatives enabled organizations to keep their operations alive, but it came at the cost of ignoring vulnerabilities present in the infrastructure. This allowed the threat actors to capitalize on this opportunity and execute their malicious intent. Additionally, a security incident has cost on average USD 4.24 million in 2021, a 10% increase from 2020, as per a report from IBM titled “Cost of Data Breach Report 2021.” This has left organizations no choice but to seek out solutions that will help them to improve their cybersecurity poweress.
One of the ways they can do that is by incorporating AIOps into their infrastructure. Using AIOps, organizations can get visibility into performance and system data on a large scale. IT helps them automate operations via multi-layered platforms while simultaneously delivering real-time analytics. Moreover, integrating AIOps in the enterprise infrastructure allows organizations to enhance their system security and resilience.
Responding to cybersecurity threats in real-time means organizations need to act fast, and to effectively do this, they need the right set of data at their disposal. Data set selection is the cornerstone of AIOps secured systems.
Today’s enterprise systems, both on-premise, and cloud, produce a lot of data noise. This results in many hackers and cybercriminals exploiting it to undetectably slip into the systems, blending with data traffic. The machine learning algorithms present in the AIOps platforms seamlessly parse the data noise at a large scale. This results in the AIOps platform creating clean curated data samples. Moreover, it empowers the ops and security teams to detect and neutralize threats and trace their movements to the penetration point.
The AIOps platform eliminates the need to curate data manually, and also helps in automation of pattern discovery in the data sets. The platform provides relevant and critical data to ops and security teams, also highlighting its relevance and use. Pattern discovery utilizes a wide range of ML techniques to extract patterns from curated data lists. In a security context, it means anything from highlighting unauthorized packets during DDoS attacks to flagging which email accounts of the organizations are opening virus-containing spam.
AIOps platforms often come with ease-of-use built-in as a core principle. If they find that the AIOps platform cannot help them to communicate its findings to a human engineer, its objectives are deemed to fail. Visualization, natural language summaries, and streamlined alerts are vital for the success of an AIOps platform.
Like any other advanced technology, the AIOps platform receives regular automated updates and maintenance that level-up defenses across the enterprise infrastructure, making it difficult for cybercriminals to execute their malicious intent.
Vishal Muktewar is a Senior Correspondent at On Dot Media. He reports news that focuses on the latest trends and innovations happening in the B2B industry. An IT engineer by profession, Vishal has worked at Insights Success before joining Ondot. His love for stories has driven him to take up a career in enterprise journalism. He effectively uses his knowledge of technology and flair for writing, for crafting features, articles and interactions for technology enterprise media platforms.
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