Your AI is only as useful as the data you let it use. Most organizations are holding back the data that matters most, because they cannot protect it and use it at the same time.
That’s the problem: It’s a business problem before it’s a security one.
Hiding a value doesn’t reduce your exposure
Most analytical platforms can mask a sensitive value when a query runs. That controls who sees a field. It doesn’t control how much identifying information sits in the environment to begin with.
With AI, that second question matters more. AI exists to combine information and find patterns. Data that can't identify a person on its own can do so once AI brings it together.
The usual fix is to obscure or remove the data until the risk goes away. That also removes the relationships AI needs. Leaders end up choosing between protecting data and using it. They shouldn’t have to.
Privacy, security, and resiliency must work as one
Teradata is introducing new capabilities across privacy, security, and resiliency.
Start with privacy. Access control decides who can see sensitive data. Identity reduction decides how much identifying information is there to see. Teradata now delivers both, through an OEM partnership with Anonomatic. Its privacy capabilities are natively available to Teradata customers.
Poly-Anonymization® replaces PII fields with a stable identifier. The underlying identity is held separately in Anonomatic PII Vault®, a post-quantum-secure environment with multiple layers of overlapping security controls. Authorized users can restore identity when a legitimate use requires it. Everyone else works with data that keeps its structure, relationships, and utility.
PII Vault does not depend on reversible encryption alone. It combines identity separation, Poly-Anonymization, access controls, and layered protections. The result is sensitive data you can use for AI, analytics, testing, and approved collaboration, without broadly exposing who the data is about.
Format-preserving encryption extends that protection where a value's structure matters to downstream systems. It protects sensitive data without breaking the applications and workflows built around it.
Now security. Protection has to follow the data. That means controls enforced at the data layer, not only in the systems around it. Teradata applies classification, policy enforcement, and access controls closer to the data itself. The result is a governance-led foundation across users, applications, and workloads.
New native capabilities extend that protection across the data layer: dynamic data masking, data classification and tagging, column-level encryption, and global identity.
Access control is where AI raises the stakes. Through a partnership with Immuta, Teradata adds dynamic ABAC. It’s identity-aware, attribute-driven access control. Policy is enforced consistently across users and AI workloads. Data isn’t duplicated. Governance rules aren’t rewritten.
For data in transit, Teradata introduces post-quantum protection through TLS 1.3 hybrid-key exchange. It combines classical and post-quantum algorithms in a single handshake. As quantum computing advances, current encryption standards become more vulnerable. Organizations that establish cryptographic readiness now will be better positioned.
Recovery means restoring the intelligence, not just the servers
A cyberattack on an enterprise running AI at scale hits more than infrastructure. It hits the data, context, and operational knowledge that AI depends on. Bringing servers back isn’t enough. The business needs its trusted context back to resume critical operations.
Teradata Enterprise Data Protection is the resiliency product in this approach. It brings backup and restore, disaster recovery, immutable backups, and database-level high availability into one operating model.
Immutable backups cannot be modified, encrypted, or deleted, even by privileged administrators. That matters because ransomware now targets backups.
Database-level high availability uses real-time replication to keep systems continuously synchronized, with low recovery objectives for mission-critical workloads. It’s distinct from disaster recovery, which serves a different recovery objective.
Match protection to the workload and the economics change
Many organizations apply the same resilience architecture to every workload. They pay for maximum availability where the business doesn’t need it.
Enterprise Data Protection lets customers align protection levels, recovery objectives, and cost to business criticality. Tiered recovery means right-sizing secondary infrastructure instead of duplicating it everywhere.
Protected data still performs at scale
Every data team asks one question before touching sensitive records: Will protection slow us down?
Teradata tested that at large scale. The workloads ran on data protected by Anonomatic-powered privacy controls. They included multi-table join queries, which depend on the relationships between records.
There was no tradeoff. Multi-table join queries on protected data used fewer Teradata resources than equivalent queries on unprotected data. Results will vary by workload and configuration.
Privacy-preserving controls can support enterprise analytics without the performance penalty most organizations expect. Protection no longer has to be the reason an AI project waits.
Where we go from here
AI environments will grow more complex. The actors inside them will multiply. Controls that are checked only at the edge will not keep pace.
Protection has to apply where data is used. Recovery has to restore what AI depends on. That’s why Teradata continues to invest in controls that govern how AI agents access and act on enterprise data within policy boundaries.
The question is no longer only who can see the data. It’s how much identity the data has to carry. Reduce that, and you can put your most valuable data to work.