The IT Nerd

Liquibase today announced that it’s opened the Liquibase 2026 Database Change Survey for IT community participation. The survey is designed for practitioners, leaders, and contributors across the applications/database ecosystem, from database administrators and developers to platform, security, and compliance teams.

This survey offers thee survey gives the readership community a voice and weigh-in opportunity on how database change governance is evolving and where the sector should focus next. The survey contains a total of 20 questions and will take about 5 minutes to complete. Respondents can provide their email for a chance to win AirPods Pro 3.

To participate, visit: https://www.liquibase.com/liquibase-2026-database-change-survey

Why This Matters to Readers: Last year’s report gathered insights from professionals across 25 countries and revealed a striking reality: fewer than 8% of organizations had achieved full DevOps maturity, while 29% remained in the early stages. The growing complexity of data environments continued to hold many teams back, and the rise of AI and ML has only intensified the challenge – 25% of immature organizations identified it as their top concern.

This year’s survey will reveal what’s changed in 2025 and help the global IT community identify emerging issues, understand their relevance to the reader’s particular organization, and assess the best practices needed to meet AI and ML challenges head-on.

Ryan McCurdy, VP with Liquibase, the leader in database DevOps, has just published “The Day a Database Permission Change Broke the Internet: A Cloudflare Story.” His analysis details how:

• A minor adjustment, routine in most organizations, touched a hidden part of Cloudflare’s architecture and awakened a dependency no one had considered dangerous.
• What happened next revealed how modern systems fail today: Nodes that loaded the expanded file went dark. Nodes that loaded the old file continued to serve traffic. The network oscillated, recovering for minutes at a time before failing again, as if trapped between two different realities.
• Once every shard of the ClickHouse cluster adopted new permissions, every file produced was oversized and every proxy that touched it entered the same panic.

The analysis details clearly and compellingly how the cascading failure occurred, and ways in which most data-driven organizations are at risk.

He notes:

“Cloudflare is one of the most capable engineering organizations in the world. Their systems are built to survive pressure that would overwhelm most companies. Their teams live in incident response. Their infrastructure is distributed, hardened, and instrumented with extraordinary detail. Yet the event that brought them down started with a quiet change in who could read what inside a database.”

He concludes by noting that the only real path forward is a new level of discipline at the data layer. Databases must be governed with the same rigor applied to application pipelines, and offers specific vendor-neutral recommendations.

Liquibase today announced new AI governance capabilities in Liquibase Secure, extending enterprise control to the database layer. The update addresses a growing blind spot in AI strategy: ungoverned database changes made by AI agents, automation scripts, and large language models that now interact directly with production data.

AI Governance Stops at the Model, but Risk Lives in the Database

As enterprises move faster with AI, most governance frameworks focus on model bias, explainability, and privacy. The greater risk often hides at the data layer. AI agents that can write or modify database queries can alter or delete production data, introduce schema drift, or corrupt AI training sets before traditional security controls ever detect them.

According to the 2025 State of Database DevOps Report, 78% of organizations struggle with AI-driven data challenges, while Gartner estimates that 40% of agentic AI projects will be canceled by 2027 if they lack clear governance at the data layer. The conclusion is unavoidable: AI governance that stops at the model is incomplete.

Liquibase Secure: Database-Layer Controls for AI Workloads

Liquibase Secure provides the automation and governance infrastructure that makes AI adoption safe, compliant, and auditable.

• Automated Policy Enforcement: Blocks destructive AI-generated changes before production across 60+ database platforms
• Role-Based Approval Enforcement: Integrates with enterprise CI/CD and access controls to ensure all database changes, including those generated by AI, are reviewed and approved prior to deployment.
• Automated Drift Detection: Identifies unauthorized schema modifications and environment inconsistencies before they affect downstream systems or model training.
• Tamper-Evident Audit Trails: Creates a verifiable record of every change for frameworks such as SOX, HIPAA, GDPR, NIST AI RMF, and the EU AI Act.
• Targeted Rollback: Reverses problematic changes in minutes instead of hours
• Schema-Level Data Lineage: Captures the full history of structural evolution, which is critical for AI model provenance and regulatory audits.

Liquibase’s observability and rollback capabilities ensure that even AI-driven changes remain explainable, reversible, and fully traceable, providing a foundation for responsible AI at scale.

Extending AI Capabilities to Database Governance

Liquibase Secure also introduces new AI-powered tools that accelerate delivery while maintaining control. The AI Changelog Generator, built from Liquibase’s frontline experience supporting enterprise database teams, converts natural language descriptions into validated changelogs that align with governance policies. It helps developers move from idea to production-ready change in seconds while preserving auditability and consistency.

The Liquibase Secure Developer Extension for VS Code brings schema management, history review, and policy enforcement directly into the IDE so developers can work faster without sacrificing traceability or compliance.

Together, these capabilities show how Liquibase is using AI to enhance governance, productivity, and developer experience across the database lifecycle.

MongoDB Partnership: Eliminating the Speed vs. Control Trade-Off

Liquibase also announced a new strategic technology integration with MongoDB, the unified data platform that powers modern, data-intensive, and AI-driven applications.

MongoDB’s flexible document model is a powerful enabler for rapid iteration and experimentation in dynamic AI environments. As agility drives growth, managing and tracking evolving schemas across many projects becomes a critical governance need. Issues like inconsistent field names or untracked schema drift can quietly disrupt analytics pipelines, corrupt training data, or derail audits over time.

Liquibase Secure integrates directly with MongoDB to provide continuous governance without slowing innovation. Every collection change runs through automated policy checks. Drift detection flags unapproved updates before they spread. Structured, tamper-evident logs deliver a single source of truth for auditors and data scientists.

Regulatory Pressure Makes Database Governance Imperative

Emerging regulations demand database-layer governance. The EU AI Act requires rigorous data traceability for high-risk AI systems. NIST’s AI Risk Management Framework establishes federal and private sector baselines. Traditional frameworks, SOX, HIPAA, PCI DSS, GDPR,  and DORA now intersect with AI workloads, creating compound compliance obligations.

Without database-layer controls, organizations face higher compliance costs, extended audits, and increased exposure to AI-amplified data errors.

Strategic Leadership: New Head of AI Strategy & Technology Innovation

Liquibase has appointed Kristyl Gomes as Head of AI Strategy and Technology Innovation, a newly created leadership role. Gomes brings more than 15 years of experience spanning database engineering, DevSecOps, and infrastructure automation.

Most recently, she served as Liquibase’s VP of Engineering, where she led development of the company’s cloud-native platform, expanded its multi-cloud footprint, and launched the first wave of AI-powered developer tools. In her new role, Gomes will guide how Liquibase applies AI across its product suite, from accelerating schema management and compliance automation to redefining AI governance at the data layer.

From Risk to Readiness

Liquibase Secure transforms databases into AI-ready systems that balance speed, safety, and compliance. By governing schema changes across platforms such as MongoDB, PostgreSQL, Snowflake, and Databricks, Liquibase helps enterprises accelerate delivery while maintaining the trust their AI initiatives depend on.

Availability

Liquibase Secure’s MongoDB integration is available today. Learn more at https://www.liquibase.com/mongodb