NETSCOUT Launch Initiative for High-Quality AI-Ready Data
Cybersecurity company NETSCOUT is making plays to make AI work better with a new initiative to address the growing demand for high-quality data in AI and AIOps solutions by enriching curated data.
Working across its partner ecosystem, which includes cybersecurity heavyweights Cisco, Palo Alto Networks, ServiceNow, Splunk, NETSCOUT plans to integrate its high-quality AI-ready data and deliver the insights needed to drive better business outcomes and elevate user effectiveness.
“NETSCOUT is built around deep packet inspection, so we understand the value of transforming raw packet data into actionable intelligence at scale,” stated Bruce Kelley, Chief technology officer at NETSCOUT.
While the AI models are becoming more sophisticated, the process of data collection and refinement often remains in the background, overshadowed by the more visible achievements of AI.
Yet, the quality of data that feeds these AI models is essential for their success, and this is where NETSCOUT’s initiative plays a crucial role.
The drama with AI data
With more and more businesses rushing to implement AI into their operations, many may run the risk of not reaching their intended outcomes. Not only is this due to the difficulty of wider digital transformations, but due to the quality of data being used to reach the levels expected.
NETSCOUT’s approach involves generating granular telemetry data at scale, which is then curated into targeted data feeds. These feeds are specifically designed to integrate seamlessly with data lakes and other AIOps platforms, enabling the data to be enriched and combined with other datasets for more effective analytics and visualisation.
This process results in higher-quality behavioural classifications, improved predictions, and more reliable automation—outcomes that are vital for any organisation looking to optimise its AI-driven operations.
NETSCOUT’s initiative is focused on delivering this intelligence by providing high-fidelity data that mitigates the risks associated with data overload. This data is crucial for identifying and correlating observability trends, streamlining data analysis, uncovering historical operational patterns, and detecting potential issues that could lead to service disruptions or security breaches.
The new refineries
In the broader industry, there is a growing concern about the potential downsides of the relentless pursuit of more data.
As AI models become more complex, the need for vast amounts of data grows, leading to the risk of systems being overwhelmed by unusable or irrelevant data.
However, flooding AIOps with poor quality data can severely compromise its effectiveness and reliability, leading to a range of detrimental outcomes. Inaccurate, incomplete, or irrelevant data can result in biased predictions.
Equally, erroneous data can mislead AI systems, causing incorrect decisions that could have serious repercussions, especially in critical sectors such as healthcare and finance.
This can increase the workload for IT teams, diverting their focus from strategic tasks to time-consuming data cleansing, ultimately leading to operational inefficiencies and higher costs.
NETSCOUT’s solution to this problem is its Omnis™ AI Insights, which delivers precise and actionable network telemetry data. This data is designed to be immediately useful, reducing the need for extensive data transformations and adaptations.
This initiative is a timely response to these industry-wide challenges. As the demand for AI continues to grow, the importance of reliable, high-quality data will only become more pronounced, making initiatives like NETSCOUT’s are essential for the continued accurate use of AI across various sectors and in AIOps.
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