娇色导航

CIOs are under pressure to deliver predictive analytics and transform the workforce with AI agents. But investments in data governance, data operations, and data security — which have always been important — have all too frequently taken a backseat to business-driven initiatives, leaving AI success today in limbo.

To address this gap and ensure the data supply chain receives enough top-level attention, CIOs have hired or partnered with chief data officers, entrusting them to address the , automate , and transform to a model focusing on health metrics, data quality,  and data model interoperability.

[ See also: 5 IT risks CIOs should be paranoid about ]

But CIOs must be careful about delegating too many data governance responsibilities or observing data operational improvements from the sidelines. CIOs rethinking their digital transformation strategies and looking to deliver business value from AI investments need to pay more attention to the data risks that can undermine these objectives. One option is to to sense where more leadership attention is needed.

I’ve previously written about what IT risks and CIOs should be paranoid about. Below are six data risks CIOs should review and ensure their teams have remediation strategies that ideally transform the dangers into strategic opportunities.

Misclassified data and disengaged data owners

Ask any data governance leader about their top struggles to improve data quality and comply with regulations. Somewhere at the top of the list will be identifying and engaging data owners in setting and complying with data policies. Without engaged data owners, data may go unclassified and used in AI, potentially breaking data privacy rules and other regulations.

In the AI era, unclassified data poses a second challenge: Should data scientists use these unclassified data sets in AI models without the required compliance, or should data governance block anyone from using this data?

“Organizations must classify their content so that various components of the security infrastructure can take appropriate actions,” says , CEO of Netwoven. “Manual classification is laborious, and auto-classification has significant false positives, so a proper balance is critical to success.”

CIOs who get involved in data governance initiatives are often more effective in persuading department heads to assign data owners. There’s too much need for citizen data science capabilities and a desire for AI business advantages for leaders to push back against taking responsibility. What’s more, once these data owners get involved, there’s more opportunity to engage them in and partner in piloting AI agents. 

Intellectual property exposed to AI

Many CIOs are concerned about shadow AI when employees use public large language models (LLMs) and experiment with other generative AI tools that haven’t been approved. Sharing misclassified data and exposing intellectual property with AI tools are risks CIOs should be paranoid about.

One concern is when in their AI prompts. Another is when data scientists include intellectual property in AI models, including in retrieval augmented generation (RAG) for LLMs and data for AI agents, without the required approvals and safeguards. 

“Data is one of the most valuable assets a company owns, and it must be protected as such,” says , global 娇色导航of Rimini Street. “CIOs should be vigilant about employees mishandling data, the misuse of AI tools, and poor cyber hygiene. Prioritizing staff education, enforcing data masking in core systems, and conducting regular data security audits are essential steps to protecting sensitive information.”

, CEO of The Modern Data Company, adds: “Marketing teams adopt ChatGPT without telling anyone, data scientists build models with whatever information they can get their hands on, and suddenly sensitive customer data might be feeding into public AI tools. This creates risks for CIOs, including data leakage and compliance issues that can be addressed through clear, practical policies and secure internal alternatives.”

To mitigate risks, Akula advises CIOs to create data products from curated, trustworthy datasets with clear ownership and governance.

CIOs can also turn this risk into an advantage by sponsoring data literacy programs and promoting open innovation where any employee can submit ideas. The combination of these programs encourages learning and problem-solving while the organization gains visibility into more initiatives from frontline employees.    

Third-party data sources

US enterprises are projected to . This data is typically used for customer segmentation, personalizing marketing campaigns, and improving attribution.

As hard as it is to ensure internal data practices meet regulatory requirements and data governance policies, tracking compliance with third-party data sources is challenging.

“One of the most overlooked risks is relying on third-party data pipelines or enrichment services without verifying how they source their data,” says , head of product at SOAX. “If that data is scraped or aggregated from public sources using unreliable or non-compliant methods, your organization could face regulatory scrutiny or operational blind spots.”

Reviewing third-party data compliance should be a communicated managed by the CDO.

CIOs can add an alternative approach to address third-party data risks. Companies procure and integrate many sources that go unused, provide duplicate information with other data sources, or deliver marginal business value. CIOs looking for cost-reduction opportunities should conduct a cost-benefit audit of third-party data sources, review utilization, and quantify risks. The opportunity is to reduce costs by eliminating low-value, higher-risk sources.   

Poor data pipeline observability

Most organizations will invest in end-user analytics tools such as and before investing in robust data integrations and pipelines. As departments rely more on real-time data for decision-making, the reliability and performance of data pipelines can be an operational nightmare, especially when data stewards must routinely fix data issues or if lagging data leads to poor decisions.      

“CIOs must remain acutely aware of data-related risks that threaten organizational integrity, security, and decision-making effectiveness,” says , CTO of RecordPoint. “One critical area is limited visibility into data pipelines and usage patterns, where insufficient observability can conceal underlying issues like latency, data drift, pipeline failures, and the presence and locations of sensitive data.”

includes monitoring data pipelines, automating responses, and tracking performance. include pipeline reliability, automation rates, exception rates, and processing throughput.

Reducing DataOps incidents can be a cost savings for CIOs who integrate many data sources and use for centralizing access. But an even more important driver is that unreliable data integrations erode trust, and that can slow department leaders from investing in more AI and data-driven practices. 

Data quality gaps

CIOs have been in a long struggle to improve data quality by assigning data stewards, automating data cleansing procedures, and measuring . But, most of this work was channeled to structured data sources in ERPs, CRMs, and data warehouses. AI increased the scope of this work as RAGs and AI agents leverage unstructured data sources and document repositories to train models and provide contextually relevant responses.

“RAG gives enterprises access to organizational knowledge, but it’s not without risks, including data privacy vulnerabilities, hallucinations, and integration challenges,” says , CEO of Pryon. “Implementation requires investing in data quality, establishing governance frameworks, and creating evaluation systems before scaling. The companies getting real value from RAG aren’t just accessing information faster — they’re making better decisions by finding the right balance between innovation and safeguards.”

To address data quality gaps, CIOs should consider centralizing raw data in data lakes, providing data cleansing as a shared service, and enabling access through data fabrics and customer data platforms. As there are many data quality and management tools, developing a shared service focused on data quality is an efficient way to address the greater business need for clean AI data sources and increased scope of cleansing unstructured data sources.  

Overindexing on AI outputs without rigorous QA

include ones on business value, tool selection, compliance, and data governance. But the most important one is also a challenge today: How should employees validate and question an LLM’s response or an AI agent’s recommendations?

This question is more important for CIOs looking to , as the quality assurance practices for are still emerging and often rely on manual testing.

“CIOs must continuously monitor the accuracy and reliability of AI-generated outputs,” says , CPO at Pendo. “Because AI systems are not deterministic, defining quality becomes more complex, and the boundaries between engineering, QA, and product begin to blur. This shift demands tighter collaboration across teams to ensure AI-powered experiences are trustworthy, aligned with user needs, and able to drive real business outcomes.”

CIOs have historically struggled to justify QA investments as it’s easier to sell the business on adding developers or increasing operational and security resiliency. But as more and IT operations become more automated, CIOs may find a greater need and budget opportunity to invest in QA and AI testing capabilities.

It’s healthy and important for CIOs to be somewhat paranoid about risks, especially around intellectual property and emerging AI capabilities. The best CIOs do more than develop mitigation plans and seek options to turn risks into strategic opportunities.