Ethical Hacking News
The modern enterprise is struggling to build trust in its data foundations, leading to significant costs and challenges in responding to disruptions. A new approach, such as building observability around MELT data enriched to provide the context that IT teams need, could provide a solution. This approach uses packet data, the authoritative record of what actually traversed the network, providing visibility into the transactions, dependencies, and interactions across the IT ecosystem. By adopting this approach, organizations can build a more trustworthy data foundation and improve their ability to respond to disruptions quickly and effectively.
Traditional MELT data lacks context, making it difficult to understand the impact of disruptions on digital enterprises. 81% of organizations believe insufficient data increases incident resolution time, and 42% estimate downtime at $500,000 to $999,000 per hour. AI-driven operations require forensic-grade data with high-fidelity context to produce reliable outcomes. Packet data provides the authoritative record of network interactions, enabling the creation of "MELT+" with metadata. Only 11% of organizations treat full-fidelity network data as authoritative. CIOs should evaluate their current observability data based on five key criteria: comprehensiveness, curation, credibility, consistency, and continuous real-time insight.
The modern enterprise is a digital enterprise, with connected systems and digital services forming the operational backbone of the organization. However, when disruption hits, it can have a significant impact on the financial, reputational, productivity, and compliance aspects of the business. This has raised observability to a board-level issue, as executives need to figure out whether a disruption is material and how to respond quickly to minimize the impact.
In many enterprises, observability is not having the desired impact, despite the long-established data foundation of metrics, events, logs, and traces (MELT). Organizations are defaulting to gathering more data, increasing sampling, and extending retention, but they're not getting better insight. The costs of this observability debt are building, with 81 percent of organizations believing that insufficient data increases incident resolution time, and over two-fifths (42 percent) estimating downtime at $500,000 to $999,000 per hour.
The main challenge is that MELT data is not designed for today's complex, distributed, and dynamic operations. Metrics explain that something has changed over time, events surface when something changed, and logs tell teams that something happened at a specific time. However, they don't provide the context that explains what actually happened on a network and why. Traces come closest, but a trace only shows what has been instrumented, which leaves it blind at un-instrumented components, third-party dependencies, and the infrastructure in between.
Context is essential, as it means being able to reconstruct a single, complete, and ordered chain of events across different systems, including what kick-started an event, how it propagated, and what happened at each step. However, research reveals that 96 percent of organizations use metrics and logs, yet 82 percent report visibility gaps, and nearly all (96 percent) lack sufficient data to determine root cause during incidents. They tend to lose visibility where systems meet, such as between on-premises and cloud, the edge, or in service-to-service interactions.
AI sharpens the challenge, as organizations are already embracing AI-driven operations to improve efficiency, decision-making, and customer experiences. However, when systems start operating autonomously, making decisions and taking action at machine speed, they need forensic-grade data with high-fidelity context to produce reliable outcomes. This demands continuous, unsampled records that preserve system interactions across environments.
The benefits of a better approach, such as building observability around MELT data enriched to provide the context that IT teams need, without the bloat that adds unsustainable extra cost, are significant. This approach starts with packet data, the authoritative record of what actually traversed the network, providing visibility into the transactions, dependencies, and interactions across the IT ecosystem.
Using deep packet inspection (DPI) techniques, this visibility can be distilled into metadata that, added to MELT, produces what NETSCOUT calls "MELT+". This approach tackles the main challenges of traditional MELT, such as scale, efficiency, cost, and data richness. It also delivers what analyst firm Futurum describes as the critical foundation for autonomous AI operations, capturing verifiable network behavior and observed interactions rather than abstractions.
Despite the obvious benefits of MELT+ approaches, only 11 percent of organizations treat full-fidelity network data as authoritative. For CIOs keen to change that statistic, the first step is to evaluate their current observability data by five key criteria, as shared by NETSCOUT COO, Sanjay Munshi. The criteria include comprehensiveness, curation, credibility, consistency, and continuous real-time insight into data in motion.
Related Information:
https://www.ethicalhackingnews.com/articles/Closing-the-Observability-Gap-for-the-AI-Ready-Enterprise-A-New-Approach-to-Building-Trustworthy-Data-Foundations-ehn.shtml
https://www.theregister.com/security/2026/09/23/sponsored-closing-the-observability-gap-for-the-ai-ready-enterprise/5298070
Published: Wed Sep 23 10:49:47 2026 by llama3.2 3B Q4_K_M
