Why AI Success Depends on Governing Unstructured Data
As companies aggressively invest in artificial intelligence (AI), many are realizing that the key to AI success is not just about sophisticated models and computing power, but rather about effectively understanding and governing the unstructured data that underpins these technologies.
The Importance of Data Governance
For unstructured data management provider Congruity360, the gap between AI ambition and readiness is a significant risk for modern enterprises. Despite the rush towards AI adoption, many organizations lack the necessary visibility into the unstructured data critical for AI success.
Data governance has emerged as a crucial factor that distinguishes organizations ready for AI from those that are not. Alarmingly, 41% of organizations report having no data-classification tools, while 37% plan to purchase such tools within the next two years. This gap leaves a substantial amount of unclassified, high-risk data spread across various environments, leading to reactive cleanup efforts that consume time, budget, and trust.
Challenges Posed by Uncontrolled Data Growth
During discussions at the Cyber Resiliency Summit, industry experts highlighted how uncontrolled data growth undermines security, compliance, and resilience. The conversation focused on the Redundant, Obsolete, and Trivial (ROT) data problem, which is not merely a nuisance but a systemic issue that can compromise business integrity.
Experts outlined the Big 4 drivers of data governance: cybersecurity exposure, compliance and legal obligations, operational efficiency, and business risk reduction. Recent findings revealed that while 70% of IT professionals have some visibility into ROT in SaaS applications, only 46% have visibility in cloud-hosted environments.
The Risks of Poor Data Visibility
Lack of visibility into data can quickly escalate risks. Forgotten credentials, delayed compliance audits, and unclassified files can increase vulnerability across environments. The pattern of poor visibility often leads to governance failures related to the Big 4 drivers.
Data security posture management emerges as a critical requirement for organizations. By identifying unstructured data attributes, companies can take actionable steps toward remediation.
ROT as a Business Risk
ROT isn’t just a storage issue; it poses significant business risks with regulatory consequences. Outdated files and abandoned shares may still contain sensitive information subject to regulations like GDPR and HIPAA. Understanding existing data is crucial for mitigating risks.
Governance Through Data Security Posture Management (DSPM)
To close visibility gaps, organizations must classify data according to business value and actively manage dark data. Congruity360 promotes DSPM as a bridge between visibility and governance, enabling organizations to identify unstructured data blind spots swiftly.
Cost pressures exacerbate security risks. Legacy systems can accumulate costs associated with inactive files and duplicates. Large enterprises often manage vast amounts of unstructured data, much of which remains unmanaged yet must comply with security and regulatory mandates.
Continuous Assessment and Governance
Congruity360 emphasizes a strategy of continuous assessment rather than episodic cleanup. By integrating classification with remediation workflows, organizations can reclaim storage, reduce exposure, and align spending with business value.
Insider risks have also intensified, particularly due to social-engineering attacks exploiting abandoned accounts. Organizations need to monitor access patterns on high-risk data to prevent accidental exposure.
The Role of AI in Data Governance
Effective management of ROT and sensitive files not only minimizes breaches but also lays the groundwork for AI initiatives that require accuracy, traceability, and trust. Clean, classified data is essential for successful AI operations.
Analysts warn that without robust information architecture, AI projects are likely to stall. Feeding AI models ungoverned or outdated data increases risks and costs, highlighting the urgency for effective data governance.
Congruity360’s Framework for ROT Control
To optimize data management, organizations need a clear understanding of their data landscape. Congruity360 outlines a framework for controlling ROT through five core components:
- Conduct regular data audits: Identify and eliminate non-critical data to reduce ROT accumulation.
- Invest in proactive classification: Use DSPM tools to quickly surface high-risk content.
- Adopt a “less is more” philosophy: Remove inactive files to reduce cost and exposure.
- Align storage with risk: Match data sensitivity with appropriate storage controls.
- Implement data lifecycle management (DLM): Define policies for aging data while preserving audit trails.
Each of these components reinforces the Big 4 governance drivers, contributing to cybersecurity, compliance, operational efficiency, and risk reduction.
The Cost of Complacency
Managing ROT is an ongoing discipline essential for survival in today’s enterprise landscape. Organizations that embrace these principles can reduce costs, simplify audits, and close breach pathways, while those that neglect them face increasing risks as AI amplifies the consequences of poor data hygiene.
