From Garbage to Great: The Data-First Path to AIOps

We’re past the days of "garbage in, garbage out." Today, just one bad dataset fed into artificial intelligence (AI) technology and—voilà: “garbage AI.” So, if AI-driven initiatives such as artificial intelligence for IT operations (AIOps) are to succeed, they need consistent AI-ready data of the highest quality. Otherwise, AI-based systems become little more than unpredictable “black boxes.”

Peeling Back the Hidden Layers of AIOps

In the previous two blog posts of this three-part series, we talked about how critical real-time and historical data are for AI. Unfortunately, most organizations realize their data is bad only after it’s already integrated into their systems.

When an AI system becomes a “black box,” it means its logic, analysis, and decisions are often erroneous and difficult for users and even the developers that built the models to understand. This lack of transparency is particularly dangerous to organizations because it makes it difficult to trust the system’s entire output, especially when the data used to train the model is flawed, biased, or incomplete. This can also create a single point of failure (SPOF) if an AI-driven process or platform misinterprets or overlooks critical issues, making the AI itself an organization-wide vulnerability.

Building prior knowledge into a machine learning (ML) model can make it more reliable. But figuring out what the model needs and how to include that isn’t always straightforward. Models may do well with training data but can struggle with real-world data that’s noisy, incomplete, or outdated. Real-world data is rarely as clean or consistent as data in controlled training environments. This can cause big problems for sophisticated AI technologies, including inefficient automation and inaccurate predictions.

For example, an AIOps and cybersecurity platform relying on incomplete logs might misinterpret a credential-stuffing attack as a normal server performance increase that typically happens during a seasonal traffic spike. This could potentially allow attackers to compromise sensitive systems undetected. Alternatively, if an automated AI program analyzes fragmented data from departments such as IT, finance, and customer service, it might produce conflicting automated actions, such as different automated responses to customer complaints. This would make the AI program hard to trust, turning it into a black box—where outcomes are more likely to be questioned than followed.

AIOps Platforms, Automation, and High-fidelity Data

AIOps and cybersecurity platforms, including those from Cisco, ServiceNow, Palo Alto Networks, and Splunk, rely heavily on advanced AI and ML algorithms to analyze machine-generated data and other sources, providing predictive insights and actionable recommendations. These AI-powered platforms depend on large datasets, making the quality of data they receive crucial. High-fidelity AI-ready data—which is accurate, trustworthy, clean, and granular—enhances the ability of AIOps and cybersecurity platforms to deliver precise and actionable insights.

False positives are very common when companies rely on less granular, accurate, and clean data to run their AI models, especially those using deep learning algorithms, which have complex layers that make it hard to see how decisions are made.

To avoid the black box problem, organizations should consider adding a few strategies to their AI roadmaps, whether they are using third-party AIOps and security platforms or building custom AI systems:

• Robust data governance: Ensure high-quality data via regular audits, validation, and cleaning processes. AIOps platforms can help automate these tasks and maintain data integrity.
• Explainable AI (XAI): Implement methods to clarify how AI-based systems make decisions, including visualizing decision pathways and explaining key factors influencing outcomes.
• Model monitoring: Continuously monitor AI-driven systems for performance anomalies and model drift, identify shifts in behavior, and ensure data integrity.
• Feedback loops: Ask users to provide input on AI performance. This can help developers refine their algorithms, reduce biases, catch errors, and enhance AI-enabled decision-making.
• Data democratization: Ensure data insights are easy to understand and can be filtered into reports specific to all stakeholders—from the chief information officer (CIO) and chief information security officer (CISO) to business executives—so they can quickly find and review the information they need.

By adopting these strategies, organizations can bring more transparency and structure to their AI-based initiatives. Or they can choose a purpose-built solution that completely removes the black box problem by ensuring that only the highest-quality data is provided from the beginning to help AI technologies consistently perform at their best.

Out-of-the-Box Insights

Omnis AI Insights, NETSCOUT’s powerful AI solution, is designed to supercharge AI initiatives, AIOps, and cybersecurity platforms. With this new offering, NETSCOUT is redefining the AI data space, providing invaluable support to businesses navigating the complexities of AI technology. Our approach generates high-fidelity, AI-ready telemetry data collected at the source and processed through deep packet inspection (DPI) at scale, providing organizations with a robust foundation for actionable insights. This data is then curated into information-rich feeds designed to tackle critical business and operational use cases. The feeds integrate seamlessly with data lakes, AIOps, and security platforms, delivering higher-quality behavioral classifications, predictive cybersecurity, and more reliable and automated business outcomes.Omnis AI Insights solutions deliver data that is not only plentiful but also clean and structured, making them ideal for AI applications. This also allows you to outpace competitors with insights that are accurate, actionable, trustworthy, and free of data silos.

That means companies can now make faster, data-driven decisions that improve business and operational efficiency, reduce risks, and drive revenue growth. Plus, AI/ML teams can build more-effective AI technologies faster than ever before—without the risk of creating unpredictable black boxes or ineffective garbage AI.

After all, if your data insights aren’t reliable or your AI technology can’t drive real business results, what’s the point?
    
Get to know NETSCOUT’s Omnis AI Insights solution and AI-ready data firsthand, and learn more about our Omnis AI Streamer and Omnis AI Sensor.