In 2026, a database being online is no longer the benchmark for success. Availability dashboards glowing green may comfort operations teams, but they say nothing about the quality, integrity, or compliance of the data flowing through those systems. If the data is stale, inaccurate, silently corrupted, or accessed in violation of regulation, the system has failed—no matter how impressive its uptime. 

Traditional Database Monitoring has focused on infrastructure health: CPU usage, disk I/O, replication lag, and failover events. Database Auditing, by contrast, has historically lived in the realm of compliance and security—tracking who accessed what data, when, and how it changed. Both disciplines matter. But in an era of AI-driven decisions, real-time analytics, and petabyte-scale pipelines, they are no longer sufficient on their own. 

At digna, we believe the next evolution is Data Observability—a shift from watching the pipes to understanding the water flowing through them. Data Observability answers a more fundamental question: Is the data inside the database reliable, trustworthy, and fit for purpose? This perspective reframes database monitoring and auditing as strategic enablers of data trust, not just operational checklists. 

Essential Database Monitoring Techniques: Ensuring Health 

Every organization still needs the operational must-haves. Query latency, throughput, and concurrency directly affect user experience and AI workloads. A slow feature store can degrade a machine learning model just as surely as a server outage. 

Key techniques include: 

• Query Latency and Throughput Monitoring: Identifying long-running or inefficient queries before they cascade into downstream failures. 

  
  

• Resource Utilization Tracking: CPU, memory, and I/O monitoring to prevent saturation and unplanned downtime. 

However, at scale, manual thresholds quickly become obsolete. Static rules—“alert if latency > 500ms”—fail in environments where workloads fluctuate hourly. This is where AI-driven baselining, calculated directly in-database, becomes essential. digna automatically learns what “normal” looks like for your data systems and adapts as usage evolves—without brittle rule maintenance. 

Schema and Structural Monitoring: Preventing Silent Failures 

One of the most underestimated risks in database monitoring is schema drift. A column added, removed, or subtly changed in type can quietly break dashboards, ETL jobs, or regulatory reports days later. 

Automated Schema Tracking ensures that structural changes are detected the moment they happen—not after a pipeline fails in production. digna’s Schema Tracker continuously monitors tables for added or removed columns and data type changes, alerting teams before downstream consumers are impacted. 

Database Auditing for Security and Compliance 

Database auditing answers the “who, what, and when” of data usage. For security teams and regulators alike, this visibility is non-negotiable. 

Core techniques include: 

• User Activity Logging: Tracking which users or services accessed sensitive tables and at what time. 

  
  

• Change Data Capture (CDC): Auditing not just that data changed, but how it changed—capturing before-and-after values for critical records. 

At petabyte scale, storing and analyzing these logs outside the database introduces latency, cost, and risk. digna’s in-database execution model keeps analysis close to the data, preserving performance and sovereignty while enabling real-time insight. 

Compliance-Centric Auditing in the European Context 

Regulations such as the GDPR and the EU Data Act demand provable control over Personally Identifiable Information (PII). It is no longer enough to claim compliance; organizations must demonstrate it. 

This requires: 

• Data Lineage and Provenance: Auditing the full journey of data from source to consumption. 

  
  

• Policy-Aware Access Tracking: Proving that sensitive data was accessed only by authorized entities and processed according to policy. 

Manual audits and spreadsheet-based lineage diagrams simply do not scale. Automated observability is the only sustainable path forward. 

Moving to Automated Data Observability 

1. From Reactive Firefighting to Proactive Auditing 

Traditional database auditing tells you what went wrong after the damage is done. digna’s approach is fundamentally different. Our AI-powered guardian model continuously observes data behavior in real time, detecting anomalies as they emerge. 

The digna Data Anomalies module leverages machine learning to identify “weird” behavior—unexpected drops, spikes, or pattern changes—without manual rule-setting. Unauthorized batch deletions, silent data corruption, or unusual access patterns are surfaced immediately, not during a post-mortem. 

2. Continuous Data Validation at Scale 

Periodic audits are snapshots in time. Modern data platforms demand Continuous Validation. With digna Data Validation, organizations can enforce business logic at the record level—checking accuracy, completeness, and consistency against a “golden record” of rules. 

Because these checks execute directly in the database, data sovereignty is preserved. Sensitive data never leaves your environment, aligning observability with privacy by design—an increasingly critical requirement under regulations like the EU AI Act. 

3. Automating the Audit Trail 

One of the most painful aspects of database auditing is the audit itself. Preparing reports for frameworks such as BCBS 239 or regulatory reviews often consumes weeks of senior data team time. 

digna transforms this burden into a push-button reality. By continuously capturing observability metrics, lineage, and validation results, digna automatically generates the evidence regulators require—reducing risk while freeing teams to focus on innovation. Historical trends and volatility analysis are further enriched through digna Data Analytics, providing deep insight into long-term data health. 

Implementation Best Practices for Database Monitoring in 2026 

• Centralize Trust Across Teams 

Database monitoring cannot live solely in IT, nor auditing solely in Legal. High-performing organizations centralize data trust on a unified platform where engineering, governance, and leadership share a single view of data health and compliance. digna was built for this convergence. 

• Alerting Without Fatigue 

Alert fatigue is the silent killer of observability initiatives. AI-powered auto-thresholds ensure teams are notified only when something truly matters—not for every minor fluctuation. This is especially critical when monitoring data timeliness across hundreds of pipelines. digna Timeliness module combines learned patterns with user-defined schedules to detect delays, missing loads, or early deliveries with precision.

• Monitor for Business Impact 

Always monitor and audit with the end user—or the AI model—in mind. If the data feeding a recommendation engine is late or incomplete, the insight is wrong. Database monitoring must therefore be aligned to business outcomes, not just system metrics. 

The Future of Database Operations 

Database monitoring and auditing are no longer optional IT hygiene tasks. They are the pillars of Data Governance, AI reliability, and organizational trust. In a world of petabyte-scale data, manual rule-setting and reactive audits are relics of a simpler era. 

Ready to implement modern database observability?

Book a demo to see how digna provides automated monitoring, auditing, and data quality assurance across your database infrastructure.