# Experian builds cyber resiliency into its AI infrastructure

> Source: <https://siliconangle.com/2026/10/28/cyber-resiliency-frameworks-enterprise-ai-infrastructure-aicybersecurityandresiliencesummit/>
> Published: 2026-10-28 21:54:04+00:00

### Experian builds cyber resiliency into its AI infrastructure

Enterprises racing to deploy AI at scale are discovering that models depend on the infrastructure beneath them. As cyber resiliency becomes [central to AI deployment](https://siliconangle.com/2026/02/18/ai-trust-strengthens-cyber-resilience-digital-age-aitrustcyberresilience/?utm_source=chatgpt.com), organizations must address foundational issues such as data quality, access controls and recovery.

That work is particularly important in data-intensive industries such as financial services. Experian PLC has built its AI strategy around infrastructure resilience, according to [Rodrigo Rodrigues](https://www.linkedin.com/in/rodrigorodriguesgoncalves/) (pictured), group chief technology officer of Experian PLC.

“Speed creates risk when foundations are weak,” he said. “Many teams add AI on top of existing systems that were never designed for it. Data comes from many systems with different rules. Logs are incomplete, backups are slow, access policies are unclear. Everything works, until it doesn’t.”

Rodrigues spoke with theCUBE’s [Christophe Bertrand](https://www.linkedin.com/in/christophebertrand/) at the [AI Cybersecurity and Resilience Summit](https://www.thecube.net/events/thecube/ai-cybersecurity-and-resilience-summit), during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed cyber resiliency frameworks for AI infrastructure, zero-trust identity for non-human agents and how Experian governs AI pipelines at scale. *(* Disclosure below.)*

### Cyber resiliency requires AI recovery and access controls

One of the sharpest lessons Rodrigues shared is that cyber resiliency cannot be assumed from the presence of backups alone. The [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework), which Experian has embedded directly into its AI pipelines, provides the structural backbone for making that governance real rather than theoretical. Before any model can deploy, data sources are checked automatically, quality rules are enforced and security policies are validated — and if any control fails, the pipeline stops with no manual override.

“Policy enforcement happens at machine speed,” Rodrigues said. “We have AI-specific recovery goals — not just for applications, but for data models and pipelines. We test AI recovery the same way we test disaster recovery: scheduled drills, clear recovery objectives, measured results. There’s no guesswork, no panic — just execution.”

The interconnected risks of bad data, compromised backups and over-permissioned AI agents form a triad of failure modes Rodrigues has tackled head-on. Bad training data does not produce a broken model immediately — it produces a model that learns the wrong patterns and amplifies them at scale.

“AI does not leak data,” he said. “Access leaks data. AI only reflects the permissions we give it. Identity must be designed for AI, not just for humans — that means applying least privilege to AI services just like users, rotating credentials frequently and monitoring access in real time with automated alerts.”

To help developers navigate this complexity without slowing delivery, Experian built an [internal AI agent marketplace](https://siliconangle.com/2026/09/04/experian-expands-into-ai-agents-with-servicenow-partnership/) — an internal registry where product and engineering teams can discover, sandbox and reuse governed agents backed by quality controls. The approach draws explicitly from lessons learned in managing microservices and application programming interfaces, Rodrigues explained, treating agents as versioned assets with defined owners, lifecycle policies and sunset criteria.

“Agentic AI can act on its own — it can scale systems, change routing, update policy,” Rodrigues said. “If this goes wrong, the impact is huge. Autonomy requires control. Simulate before execution. Limit agent permissions until you have a strong foundation and have learned how to operate safely.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the [AI Cybersecurity and Resilience Summit](https://www.thecube.net/events/thecube/ai-cybersecurity-and-resilience-summit):

*(* Disclosure: TheCUBE is a paid media partner for the AI Cybersecurity and Resilience Summit. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)*

##### Photo: SiliconANGLE

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