Whether you are looking at customer service, employee experiences, or complex IT operations, odds are AI has made its way into those areas in some form. The technology has seen an explosion in adoption that spans industries and functions to the point where AI is playing a major role in driving decision-making, automating operations, and personalizing customer experiences.

As AI, at enterprise scale, shows significant promise, businesses are scrambling to capture that value as quickly as possible. In fact, a Cloudera survey found that more than half (52%) of respondents had been successful in realizing measurable value from AI initiatives. But AI is not a risk-free endeavor, and fully embracing AI without the right foundation in place can leave organizations open to security and compliance lapses.

This is because AI models require vast quantities of data—often encompassing sensitive customer information or other proprietary data—to generate insights and uncover opportunities. But if that data is compromised, whether it’s a breach that exposes personally identifiable information or a cyber-attack that lets bad actors into critical systems, the AI outputs simply cannot be trusted.

Enterprises are realizing that cybersecurity is more than just a defensive tool. It’s an essential part of an organization’s data foundation that makes scalable, responsible, and trustworthy AI possible.

Start with data management

Enterprises today are responsible for more data than ever before—and that data is not uniform or kept in one place. Data may be structured, unstructured, or semi-structured, and scattered across clouds, data centers, and even throughout edge devices. More data brought to AI improves accuracy and helps fuel success. But if that data is not adequately secured in the process, the number of attack surfaces increases dramatically.

This reality makes identifying and adopting the right security solutions to enable effective governance, data quality controls, and visibility, core to standing up an impactful AI operation. But it’s also not something that can be treated as an add-on. In short, it means building systems where data protection is automatic, auditable, and continuous. Oftentimes, cybersecurity conjures images of defense or reactive activities aimed at keeping systems secure. But securing AI has to be about getting proactive and embedding security into every layer of infrastructure.

Building trust through security

So where does that start? One of the most important components to start building a trusted foundation for AI lies in a unified data fabric. Adopting this approach, organizations can tap into disparate data sources across hybrid cloud environments and develop a comprehensive understanding of the contents. Cloudera’s data lineage capabilities allow organizations to trace every data asset across its lifecycle: where it came from, how it was transformed, and where it’s being used.

This transparency transforms security from passive oversight to active governance. When every dataset and model interaction is traceable, compliance becomes automatic, and breaches are easier to detect. Visibility and control replace blind trust.

This not only strengthens compliance but also ensures that every data set feeding an AI model is accurate, approved, and authorized. The result being that enterprises gain full lifecycle protection—from data ingestion to model output.

Achieving AI at scale without adding risks

As AI becomes the engine of enterprise innovation, security must be a key consideration from the start. Building a secure, governed data foundation transforms cybersecurity from a barrier to progress into a catalyst for responsible growth.

When organizations embed protection, visibility, and governance across every layer of their data ecosystem, they don’t just mitigate risk; they accelerate innovation with confidence.

Learn more about how Cloudera is helping enterprises create a strong foundation—built with unified data management, security, and lineage within a single platform—to scale trusted AI integration.

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