Closing the observability gap for the AI-ready enterprise

The modern enterprise is a digital one, with connected systems and digital services forming its operational backbone. Disruptions, such as cyber-attacks, DDoS attacks, or bad network updates, can have significant financial, reputational, productivity, and compliance impacts. Observability has become a board-level concern, with material cyber incidents requiring disclosure for public companies.

The first executive problem when a disruption occurs is determining whether it is material. Current data foundations of metrics, events, logs, and traces (MELT) struggle to keep pace with today's complex and scalable digital infrastructure. Organizations have increased data gathering, sampling, and retention, but this has not improved insight.

Executives often trust their gut more than expected given the amount spent on data. Insufficient data increases incident resolution time for 81% of organizations, with downtime estimated at $500,000 to $999,000 per hour. To leverage autonomous AI in operations, organizations need a trustworthy data foundation. This requires a data foundation that is consistent, comprehensive, enriched, and real-time, delivered in a way that complements existing observability investments.

MELT data is still essential but was not designed for today's complex, distributed, and dynamic operations. While traces capture what has been instrumented, they leave out un-instrumented components, third-party dependencies, and infrastructure in between. Timestamps can be inconsistent, identifiers might not be preserved across architectural boundaries, and data may be stored across incompatible tools.

AI further complicates the issue, requiring forensic-grade data with high-fidelity context for reliable outcomes. Building observability around MELT data enriched with network packet data can provide the missing context without adding unsustainable costs.