The IT Nerd

Liquibase today unveiled Liquibase Change Intelligence and a new suite of Liquibase Secure Deployment Connectors, expanding how enterprises understand, govern, and operationalize database change across modern delivery environments.

The new capabilities are designed to help teams understand database changes, monitor delivery performance, identify risk earlier, resolve issues up to 95% faster, and centralize audit evidence, while extending governed database change into the systems where developers, DBAs, and change teams already work, including ServiceNow, GitHub, Harness, and Terraform.

The announcement addresses a persistent gap in enterprise delivery. While application and infrastructure changes have become more automated, observable, and standardized, database change still too often moves through ticket attachments, side-channel SQL, manual approvals, and inconsistent execution paths. The result is slower investigations, weaker auditability, and more risk around outages, data integrity, and compliance.

Change Intelligence helps teams see what changed and respond faster

Liquibase Change Intelligence is designed to give teams a clearer view of what changed, how changes are moving across environments, where drift is emerging, and what requires attention next.

It brings together deployment activity, environment-level change status, drift signals, policy outcomes, and operational history so teams can answer critical questions faster: What changed? Where did it fail? Which environments are out of sync? Is drift increasing? What needs to be fixed now?

When failures occur, Change Intelligence is designed to help teams investigate with greater speed and context through AI-driven analysis that identifies likely causes and provides remediation guidance. Instead of forcing teams to reconstruct events from scattered logs, tickets, and tribal knowledge, it gives them a more direct path from issue to understanding to action.

Change Intelligence is also designed to help organizations centralize audit evidence for what changed, who approved it, where it ran, and what happened. That gives engineering, security, and compliance teams a more structured and accessible record of database change activity, reducing reliance on screenshots, manual evidence gathering, and fragmented reporting.

New connectors extend governed database change into the tools teams already use

Liquibase also unveiled a new suite of Liquibase Secure Deployment Connectors designed to extend governed database change into the platforms many enterprises already use to plan, approve, and deliver work.

For teams using ServiceNow, the connector is designed to bring database change into the existing approval process so approved tickets can result in governed, auditable deployments instead of manual SQL execution and disconnected handoffs.

For teams using GitHub, the connector is designed to bring database change into the same pull request and workflow model already used for application code, adding policy checks, validation, and deployment history tied to commits and branches.

For teams using Harness, the connector is designed to preserve existing pipelines while adding stronger governance, centralized visibility, and compliance-grade auditability around database changes.

For teams using Terraform, the connector is designed to extend infrastructure as code to the database layer, connecting Liquibase Secure to Terraform-managed instances through existing pipelines while enforcing database policies, applying versioned changeSets, and maintaining a complete audit trail over time.

Together, the connectors are designed to remove one of the biggest barriers to stronger database governance: the belief that teams need to rebuild their workflows to get it. Instead, Liquibase is extending governed database change into the systems teams already use, while strengthening traceability, standardization, and audit evidence across the delivery lifecycle.

Built for a new era of AI, data integrity, and operational accountability

The new capabilities reflect a broader shift in how enterprises are thinking about AI readiness and operational risk.

As AI initiatives expand, more changes are being generated, reviewed, and pushed through delivery systems at higher speed and greater scale. But when database change remains inconsistent, weakly governed, or hard to trace, the resulting risk does not stay isolated at the database layer. It carries into applications, analytics, automation, and AI-driven systems.

By helping organizations better understand database changes, catch drift earlier, investigate failures faster, and centralize audit evidence, Liquibase is giving enterprises a stronger operational foundation for trusted applications, data products, and AI initiatives.

Availability

Liquibase Change Intelligence, Liquibase Secure Deployment Connectors, and related capabilities are expected to begin rolling out in fall 2026. Additional details will be shared closer to availability.

Liquibase, the leader in Database Change Governance, today released the 2026 State of Database Change Governance Report, new research on how enterprises are managing database change as AI becomes embedded across production systems, analytics, and delivery pipelines. The report finds that AI interaction with enterprise databases is now widespread, while governance automation and consistent enforcement have not kept pace with the speed and scale of change. (The report and graphic are linked at bottom.)

For CIOs, the issue is not that AI touches production data. The issue is whether the organization can prove control at the database layer when change is frequent, environments are heterogeneous, and AI introduces new pathways for change and access. At AI scale, manual governance struggles to keep up. That is where risk compounds and then surfaces as data quality failures, audit friction, and outcomes leaders cannot explain.

Key survey findings:

• AI interaction: 96.5% of respondents report at least one AI or LLM interaction with their production databases, including analytics and reporting, training pipelines, internal copilots, and AI-generated SQL.
• Change velocity: 68.1% deploy database changes weekly or faster, including 10.8% deploying multiple times per day and 18.8% deploying daily.
• AI-era risk: 64.3% cite data quality issues as a top AI-related risk, and 46.5% cite ungoverned AI-generated SQL as a key concern.
• Estate complexity: Organizations report an average of five database and data platform types, and 29.1% manage ten or more database types.
• Governance gap: Only 28.1% report database change governance that is standardized and consistently enforced, while 42.3% remain at Ad hoc or Emerging. Only 7.7% report fully automated governance using policy as code with real-time enforcement.
• Audit pressure compounds the challenge. The report finds 95.3% of respondents undergo multiple compliance or database audits per year, with more than one in five facing seven or more audits annually.

The report highlights a widening operating gap. Enterprises are shipping database change continuously across diverse platforms, while governance often depends on documentation, manual review, and fragmented evidence. In an AI era, those approaches do not scale. As AI automations and AI-generated changes increase, the cost of inconsistent enforcement rises, and the blast radius of a single unmanaged change expands across downstream analytics and AI systems.

What customer behavior telemetry shows at AI scale:

Anonymized Liquibase Secure product telemetry, separate from the survey results, reveals the following.

• Governance is the default: 99.25% of Liquibase Secure sessions run with governance enabled, a necessary baseline as AI increases the volume of proposed change.
• Standardization enables automation: Nearly 86% of observed changelog activity is in XML and YAML, supporting machine-readable change definitions that AI-scale delivery can validate and enforce.
• Controls must exist before CI: About 90% of sessions run outside CI, reinforcing that as AI accelerates change, governance has to shift left into the developer workflow.
• Adoption starts with proof: Reporting is among the most exercised capabilities, reflecting early demand for audit-ready traceability as AI makes decisions harder to defend without evidence.

A practical roadmap and scorecard for CIOs

Beyond the survey findings, the report provides a staged operating model for moving from ad hoc database change to standardized, enforced, and observable governance, without slowing delivery. It also introduces a CIO-ready scorecard that pairs reliability metrics (MTTD and MTTR) with coverage metrics for automated controls, audit evidence, and AI-governed change, so leaders can measure progress and risk reduction over time.

Here’s a link to a summary of the 2026 State of Database Change Governance Report.

Liquibase, the leader in Database Change Governance, today announced the release of Liquibase Secure 5.1, extending modeled Change Control to Snowflake. With 5.1, enterprises can govern Snowflake control plane changes with the same rigor and automation they already apply to schema evolution, closing a critical gap in data platform security, compliance, and AI readiness. Liquibase Secure 5.1 also expands database platform coverage, including new support for additional cloud and enterprise data stores.

Snowflake has become mission-critical infrastructure for analytics, data products, and AI initiatives. As organizations scale DataOps and internal developer platforms, Snowflake changes are no longer isolated technical updates. They are platform-level changes that impact trust, availability, and every downstream consumer. Yet many of the most consequential changes still happen outside standardized governance, often delivered as scripts with limited visibility, weak enforcement, and evidence that is difficult to assemble when it matters most.

Modeled Change Control for Snowflake

Liquibase Secure 5.1 treats key Snowflake control plane changes as first-class, modeled change types, rather than opaque scripts. That modeling enables precise policy enforcement, object-aware drift detection, and audit-ready evidence at the level where access, movement, and execution are defined.

With Liquibase Secure 5.1, data platform teams can govern Snowflake changes across access and security configuration, data sharing and movement, platform and cost controls, and automated execution, using standardized workflows across environments and teams.

Key outcomes include:

• Stop risky Snowflake control plane changes before they reach production
• Standardize how Snowflake changes are delivered across environments and teams
• Automatically generate audit-ready evidence for every change
• Detect drift and out-of-band updates to governed Snowflake objects
• Recover faster with traceable, reversible changes and tested rollback procedures

This closes a long-standing gap for organizations that govern schema evolution, yet still struggle with over-permission creep, ungoverned data movement, and control plane drift that can undermine security posture and AI initiatives.

Built for DataOps, data products, and AI readiness

As Snowflake increasingly powers feature engineering, model training, and AI-driven decisioning, the blast radius of ungoverned change grows. A single access change can expose sensitive training data. An unreviewed sharing update can expand compliance scope. An execution change can silently alter business-critical logic. Liquibase Secure 5.1 helps data platform teams keep Snowflake predictable, auditable, and reliable as usage scales, without turning governance into a bottleneck.

Expanding database support across Liquibase’s industry-leading coverage

Liquibase Secure continues to deliver broad database coverage across 60+ platforms, from mainframe DB2 to cloud-native data stores. Liquibase Secure 5.1 expands support for Snowflake, Databricks, and MongoDB, and adds new platform support for Couchbase, AWS Keyspaces, DataStax Enterprise, and AlloyDB for Google Cloud. This breadth helps enterprises standardize change governance across heterogeneous environments using a single platform instead of stitching together siloed tools and processes. Teams can apply consistent workflows and generate unified, audit-ready evidence across their database estate, reducing operational overhead while preserving the flexibility to adopt new technologies without rebuilding governance each time.

Enterprise partnership, not just tooling

Liquibase brings more than a decade of frontline experience helping enterprises govern database change at scale. In addition to the platform, Liquibase provides hands-on professional services, a dedicated customer success organization, and ongoing advisory support to help teams operationalize Change Control across their delivery model.

Availability

Liquibase Secure 5.1 is available now. To learn more about Change Control for Snowflake and Database Change Governance, visit liquibase.com.

Liquibase today announced fiscal year 2025 momentum driven by accelerating new customer demand, record Liquibase Community adoption, and continued operating discipline.

Late 2025 outages across major internet services were a reminder that change can cascade at scale into widespread disruption. As AI pushes more automation downstream, database changes increasingly require enforcement before production and evidence after release.

Database Change Governance is the enforcement and evidence layer for database change. It prevents risky changes from reaching production and produces proof of what ran, where, and when after release, so teams can ship faster without sacrificing control. Without it, a breaking schema change can ripple across applications, data products, and automated workflows.

FY25 momentum highlights

1. New ARR increased more than 85 percent year over year
2. Liquibase Community surpassed 15 million downloads in 2025
3. Liquibase launched Liquibase Secure and Liquibase 5.0 in FY25
4. Operating efficiency improved dramatically over the last few years, strengthening operating leverage and execution discipline.
5. Liquibase Secure won the 2025 DevOps Dozen Award for Best DevOps for DataOps and Database Solution
6. Liquibase was also named a finalist in three DevOps Dozen categories: DevSecOps, Database DevOps, and Mainframe Modernization
7. Expanded platform partnerships with Databricks and MongoDB to bring governed database change to modern data platforms and AI driven applications.

Liquibase also expanded its ecosystem partnerships to meet teams where database change is happening, inside modern data platforms and AI driven applications. In 2025, Liquibase partnered with Databricks to bring modern change management to the lakehouse and announced a strategic technology integration with MongoDB to bring governance to AI driven database changes.

Governance customers use in real delivery workflows

Liquibase Secure helps teams ship database change with guardrails and proof. Teams use Policy Checks to enforce policies before changes reach production, and Reports to generate audit ready evidence of what was applied, where, and when. Together, these governance capabilities integrate into automated deployments, reducing late stage surprises and making releases more predictable.

Customer feedback reinforces this. As one TrustRadius reviewer, a Senior Configuration Management Advisor, put it: “Liquibase fixes a problem everyone has but doesn’t know there’s an answer for.”

Industry recognition

Liquibase Secure was named the winner of the 2025 DevOps Dozen Award for Best DevOps for DataOps and Database Solution.

Leadership additions to scale the next phase

Liquibase strengthened its leadership team in FY25 to support product velocity, enterprise execution, and international growth.

1. David De Paula, VP International Sales, former VP Sales, EMEA and APAC at CloudBees
2. Mike Runco, VP Sales, North America, former VP of Sales at UnifyApps
3. Ryan McCurdy, VP of Marketing, former SVP of Marketing at Astronomer
4. Steve Surace, VP of Engineering, former VP of Engineering at Datto

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