Top Data Integrity Threats And Practical Ways To Counter Them

Across industries, AI models and analytics increasingly drive high-stakes decisions—but even the most advanced systems can be useless, or even harmful, if the data feeding them is flawed. Many organizations overlook subtle yet serious risks to data integrity, from silent schema drift and redundant legacy systems to poorly managed access controls and shadow data.

Left unaddressed, these issues can erode trust, distort insights and expose companies to compliance and security threats. Below, members of Forbes Technology Council share often-missed threats to data integrity that can undermine even the most sophisticated systems, along with practical strategies for detecting, preventing and mitigating them before they cause lasting damage.

1. ‘Zombie Apps’

A growing but overlooked threat to data integrity is the rise of “zombie apps”—unused legacy systems still running in the background. These systems often contain unmonitored, outdated and incomplete data, creating blind spots and risk. They also drain resources at a time of rising cost pressure and security threats. Regular audits and expert-led decommissioning are essential to address them. - Jason Rose, Clearsense

2. Data Entry Errors

A common risk to data integrity is simple human mistakes when typing or recording data. Even small errors can cause big problems in research. Using electronic systems with automatic checks, double-checking entries and regular training helps catch these mistakes early and keeps the data trustworthy. - Jacques Nack, JNN Group, Inc.

3. ‘Shadow Data’

A common risk is “shadow data”: spreadsheets and ad hoc files living outside official systems, causing version drift, errors and no audit trail. Mitigate this risk by enforcing a single source of truth with built-in automation, validation rules and access controls and by mapping data flows and continuously training users. - Andrei Danescu, DEXORY

4. Lack Of Data Governance

One overlooked risk to data integrity is a lack of data governance during early AI adoption. Responding to our inaugural AI survey, 52% of IT leaders cited data governance as a top concern. To ensure accurate, secure and scalable AI outcomes, companies must enforce strong policies for data governance, tagging and enforcement. - Justin Mescher, ePlus

5. Poorly Managed User Access Controls

Companies often overlook risks from poorly managed user access controls. Excessive or outdated permissions can lead to unauthorized data changes or breaches. Mitigate this with strict role-based access controls, regular audits of user permissions, and adherence to the principle of least privilege. - Kevin Meyer, Pure (YC23)

6. AI Data Degradation

One overlooked risk to data integrity is the silent degradation of AI systems. Machine-generated data via models, LLMs or auto-labeling is often reingested without oversight, creating feedback loops and drift and reducing trust in data. To mitigate this, organizations must extend data quality practices to include AI observability and treat machine-generated data as a governed asset. - Anusha Dwivedula, Morningstar

7. Manual Processing Errors

A frequently overlooked risk to data integrity is human error during data entry and manual processing, which can compromise data accuracy. Mitigation involves implementing rigorous data validation, process automation, strict access controls and continuous employee training. Additionally, audit trails and version control are essential for ensuring accountability and enabling data recovery. - Ilakiya Ulaganathan, JPMorganChase

8. Insider Threats

Threats from individuals within an organization, whether due to unauthorized access, negligence or shadow IT usage, can pose significant risks. These risks are often overlooked in an organization because employees are inherently trusted. - Ganesh Kirti, TrustLogix

9. Excessive Data Collection

When it comes to data, businesses need to collect less of it. It’s too easy to think more data means better decisions. The reality is, the more data points you have, the more potential integrity issues arise, the greater the maintenance overhead becomes, and the easier it is to tell the wrong story as complexity grows. Intentionally constraining the available data points acts as a forcing function that ensures each one matters. - Trevor Hinesley, Soundstripe

10. Physical Security Breaches

Companies often overlook how physical security breaches can quickly escalate into data integrity threats. Unauthorized access to facilities, unmonitored areas or lapses in on-site surveillance can lead to compromised systems or stolen credentials—all of which can jeopardize sensitive data. AI surveillance can detect and alert on unusual behavior or entry attempts, helping prevent compromise. - Dmitry Sokolowski, VOLT AI

11. Disjointed Customer Data Governance

The lack of a consistent, governed view of the customer across systems is a common risk to data integrity. Without clear ownership and governance controls, companies end up with disjointed records. Establishing a data governance program and regularly auditing its effectiveness is critical for building a trusted customer data foundation that supports better decisions and stronger relationships. - Ann Blakely, Baker Tilly

12. Reliance On Unverified Data

One of the most overlooked risks to data integrity is relying on unverified data or unclear sources. Without validation, context, governance or timeliness, even well-designed systems can produce misleading outputs. Integrity starts with disciplined input—otherwise, everything downstream is suspect. - Eddy Azad, Parsec Automation

13. Silent Schema Drift

A common but overlooked risk to data integrity is silent schema drift—small, unalerted changes in upstream data that quietly break pipelines or corrupt downstream analytics. The fix is to use automated data contracts, CI/CD schema checks and monitoring to catch issues early before bad data spreads. - Jiazhen Zhu, Walmart

14. Synthetic Data Feedback Loops

As AI-generated content floods enterprise systems, a growing risk to data integrity is synthetic data feedback loops. When models are trained on data that was itself AI-generated, quality and trust erode over time. Enterprises must invest in provenance, validation and infrastructure that distinguishes real data from synthetic data at scale. - David Gucker, Vultr

15. Data Silos

Data silos are the biggest overall risk to data integrity and cybersecurity. Many companies have adopted point solutions to address specific needs or manage particular tasks, creating a patchwork of disconnected technologies and inefficient data silos. Organizations need to unify operations through platform-based solutions to improve connectivity and provide high-quality, well-managed data. - Mark McDonald, CoStar Real Estate Manager

16. Nonstandardized Data Formatting

A common but overlooked risk to data integrity is inconsistent data entry and formatting across systems, especially when integrating third-party or legacy data. This creates silent errors that cascade through analytics and AI models. To mitigate it, implement strict data validation rules, standardized schemas and automated quality checks at every ingestion point. - Arpan Saxena, basys.ai

17. ROT Data

One of the most overlooked threats to data integrity is the silent sprawl of redundant, obsolete and trivial (a.k.a. ROT) data. It clutters storage, obscures critical assets and increases the blast radius of a breach. The answer is intelligent data classification and defensible deletion, because protecting everything means protecting nothing. - Carl D’Halluin, Datadobi

18. Fragmented Data Stewardship

A silent risk is when ownership of setting data policy is separated from execution. When legal, product and data teams operate in silos, governance breaks. Solve it with a unified data stewardship model that is cross-functional, accountable and tuned to compliant data practices, evolving privacy laws and product velocity. - Jagbir Kaur, Google

19. Compromised Backup Systems

Companies are often not aware that their backup and storage systems may be compromising the security and integrity of their stored data. Without full confidence in backup integrity, recovery is at risk. Immutable storage, combined with a modern, multilayered cybersecurity strategy grounded in zero-trust principles, is the only way to truly protect data from being corrupted or altered under any circumstances. - David Bennett, Object First

20. Ignoring Minority Or Outlier Sentiment Data

A major risk companies overlook is ignoring minority or outlier sentiment data and focusing only on majority trends. Dismissing these quiet signals can hide deep-rooted systematic issues, usability gaps or emerging risks that spike later. Prioritizing inclusive, holistic data analysis helps uncover early warning signs and strengthens long-term integrity. - Morgan Shuler, Tapplix Applications & Web Design