New capabilities protect the sensitive data and intelligence behind enterprise AI while keeping mission-critical operations available and recoverable

SAN DIEGO, Oct. 20, 2026 /PRNewswire/ — Teradata (NYSE: TDC) today announced new privacy, security, and resiliency capabilities designed to address one of the defining challenges of enterprise AI: how sensitive data is protected, who and what can access it, and whether it remains available and recoverable when something goes wrong. Teradata addresses all three through an integrated approach that reduces raw identity exposure, enforces protection closer to the data, and keeps the intelligence powering AI available and recoverable. In practice, that means protecting the intelligence behind AI without diminishing what makes it valuable.

Key Capabilities

Poly-Anonymization®: reduces raw identity exposure while preserving the relationships and utility AI and analytics requireFormat-preserving encryption: protects sensitive values without changing their format or breaking downstream systemsPost-quantum security and readiness: strengthens protection against emerging quantum-era threats through Anonomatic PII Vault® and TLS 1.3 hybrid-key exchange for data in transitImmutable backup protection: prevents recovery points from being modified or deleted, strengthening cyber-recovery readinessDatabase-level high availability: an additional availability layer for mission-critical workloads, distinct from disaster recovery

Why It Matters Now
As AI becomes mission-critical, it expands both the value and the vulnerability of enterprise data. Sensitive information now moves among users, applications, models, and agents, making it harder to secure at the boundary alone. AI is designed to combine disparate information to find patterns and make decisions, which means data that isn’t individually identifiable can become so when AI brings it together.

Executive Quote
“Privacy, security, and resiliency have historically been treated as separate problems with separate solutions. That approach doesn’t hold up when AI is moving sensitive data across users, applications, models, and agents simultaneously. This announcement reflects how we think those capabilities need to work together, and it’s the foundation for how we intend to extend that protection as AI environments grow more complex and the actors operating within them multiply.”
          -Sumeet Arora, Chief Product Officer, Teradata

Privacy Protection
Access control determines who can see sensitive data. Identity reduction changes how much identifying information needs to be present in the analytical environment in the first place. Teradata combines both, through an OEM partnership with Anonomatic, whose privacy capabilities are natively available to Teradata customers.

Poly-Anonymization replaces PII fields with a stable identifier while holding underlying identity separately within Anonomatic PII Vault, a post-quantum-secure environment built with multiple layers of overlapping security controls designed to protect sensitive identity information. Authorized users can restore identity when a legitimate use case requires it, but everyone else works with data that retains its analytical structure, its relationships, and its utility. The result is an analytical environment where sensitive data can be used for AI, analytics, testing, and approved collaboration without broadly exposing who the data is about.

Unlike approaches that rely solely on mathematically reversible encryption, PII Vault combines identity separation, Poly-Anonymization, access controls, and additional layered protections to reduce identity exposure while retaining the utility required for AI and analytics. Format-preserving encryption extends that protection to situations where a value’s structure matters to downstream systems, protecting sensitive data without breaking the applications and workflows built around it.

Testing at large scale demonstrated that Teradata’s Anonomatic-powered privacy-preserving controls can support enterprise analytical workloads without the performance tradeoffs organizations typically associate with data privacy. Multi-table join queries on protected data used fewer Teradata resources than equivalent queries on unprotected data, though results will vary by workload and configuration.

Partner Quote
“Enterprise AI creates a fundamentally different privacy challenge. Organizations need to use sensitive, individual-level data to build effective AI, but the traditional tools for protecting that data weren’t designed to preserve what AI needs to work. Our partnership with Teradata makes it possible to reduce identity exposure at enterprise scale without forcing organizations to choose between protection and utility.”
          -Matt Fleck, Founder and CEO at Anonomatic, a data security software company focused on making PII data privacy secure, simple and cost effective

Native Data Security
Protection that follows the data requires controls enforced at the data layer, not just at the systems surrounding it. Teradata applies classification, policy enforcement, and access controls closer to the data itself. Together, these capabilities provide a governance-led foundation for protecting data and intelligence across users, applications, and workloads.

For data in transit, Teradata introduces post-quantum protection via TLS 1.3 hybrid-key exchange, which combines classical and post-quantum cryptographic algorithms in a single handshake. As quantum computing advances make current encryption standards increasingly vulnerable, organizations that establish cryptographic readiness now will be better positioned for what’s ahead.

New native capabilities including dynamic data masking, data classification and tagging, column-level encryption, and global identity extend that protection across the data layer. Through a partnership with Immuta, a data authorization platform between identity and enterprise data, dynamic ABAC brings identity-aware, attribute-driven access control that enforces policy consistently across users and AI workloads without duplicating data or rewriting governance rules.

Teradata’s security architecture is designed to extend as AI environments evolve, with ongoing investment in controls that govern how AI agents access and act on enterprise data within policy boundaries.

Enterprise Data Protection
For enterprises running AI at scale, a cyberattack is a threat to the data, context, and operational knowledge that AI depends on to function. Teradata Enterprise Data Protection addresses that reality through a workload-aligned approach that lets organizations match protection levels to business criticality rather than applying the same resiliency architecture to every workload. The objective is not simply to restore data, but to restore the trusted context AI needs to resume mission-critical operations.

Database-level high availability via real-time replication introduces an additional availability layer designed for continuous synchronization and low recovery objectives.

Immutable backup protection strengthens cyber-recovery readiness by ensuring recovery points cannot be modified, encrypted, or deleted, even by privileged administrators. As ransomware increasingly targets backup data specifically, protected recovery points have become as critical as the protection strategies built around them. Teradata’s resiliency architecture is designed to extend as cyber threats and recovery requirements evolve.

Additional capabilities including backup and recovery, scheduled data synchronization, and disaster recovery round out a more consistent Enterprise Data Protection experience.

Availability

Privacy capabilities, powered by Anonomatic and delivered by Teradata, are available now for Teradata Cloud on AWS, Azure, and Google Cloud.Native data security capabilities including dynamic data masking, data classification and tagging, and column-level encryption are available on Teradata Cloud across AWS, Azure, and Google Cloud. Dynamic data masking, data classification and tagging are available for Teradata Factory and on-premises deployment options.Teradata Enterprise Data Protection including immutable backup protection is available now on Teradata Cloud across AWS, Azure, and Google Cloud with availability on Teradata Customer Cloud planned in the coming months. 

About Teradata
Teradata empowers enterprises to turn intelligence into autonomous action, grounding AI agents in deep business context and trusted data. As AI agents multiply, Teradata is the context foundation, governance layer, and performance backbone that companies need now. The Teradata Autonomous Knowledge Platform puts AI into production across cloud, on-premises, and hybrid environments.

Forward-Looking Statements
This press release contains “forward-looking statements” about the expectations, beliefs, plans, and intentions relating to Teradata’s privacy, security, and resiliency capabilities. Such statements include statements regarding future product availability, capabilities, and offerings, and expected benefits to Teradata customers. Forward-looking statements are subject to known and unknown risks and uncertainties and are based on potentially inaccurate assumptions that could cause actual results to differ materially from those expected or implied by the forward-looking statements. If any such risks or uncertainties materialize or if any of the assumptions prove incorrect, Teradata’s results could differ materially from the results expressed or implied by the forward-looking statements made. Teradata undertakes no obligation, and does not intend to update the forward-looking statements. Factors that may cause actual results to differ materially from those in any forward-looking statements include: (i) delays and unexpected difficulties and expenses in executing the planned product capabilities and offerings, (ii) changes in the regulatory landscape related to AI and (iii) uncertainty as to whether customer adoption will justify the investments in the product capabilities and offerings. Further information on factors that could affect Teradata’s financial and other results is included in the filings Teradata makes with the Securities and Exchange Commission from time to time.

The Teradata logo is a trademark, and Teradata is a registered trademark of Teradata Corporation and/or its affiliates in the U.S. and worldwide.

Poly-Anonymization and PII Vault are registered trademarks of Anonomatic, Inc.

MEDIA CONTACT
Jennifer Donahue
Jennifer.Donahue@Teradata.com

View original content:https://www.prnewswire.com/news-releases/teradata-introduces-new-privacy-security-and-resiliency-capabilities-as-ai-forces-change-in-enterprise-data-protection-302913579.html

SOURCE Teradata

New Teradata Factory and Teradata Sovereign Cloud on Google Distributed Cloud capabilities give enterprises greater control over where their most sensitive data and AI operate

SAN DIEGO, Oct. 20, 2026 /PRNewswire/ — Teradata (NYSE: TDC) today announced a portfolio of deployment and platform capabilities designed to give governments and enterprises greater control over the data, AI, and technology that power their most critical operations. This includes expanded capabilities for Teradata Factory with AI Studio and the availability of Teradata Sovereign Cloud on Google Distributed Cloud air-gapped environment.

What’s New
Teradata’s portfolio spans two complementary deployment models designed to address different sovereignty requirements.

The expanded capabilities for Teradata Factory with AI Studio bring a unified AI environment to operate on data, vectors, models, and agents directly into customer-controlled on-premises infrastructure. These capabilities include:

Tera, the agentic coworker for enterprise data work, including Tera Harness and Tera Context EngineAgentStack, the complete AI agent development capabilities to build, manage, observe and operate agents through natural language, pro-code, low-code, and Model Context Protocol (MCP) interfacesModel services, which provide machine learning operations and AI model management with pre-integrated model and bring-your-own-model supportData pipelines for ingesting multimodal unstructured data, vector embeddings, vector storage and hybrid search for RAGAnalytics, native notebooks backed by in-database analytics and Teradata’s Elastic Python Compute, built on Ray and Arrow, bring AI to where data lives

Teradata Sovereign Cloud on Google Distributed Cloud air-gapped brings Teradata’s high-performance enterprise-scale analytics into fully isolated environments. Key benefits of running Teradata Sovereign Cloud on Google Distributed Cloud air-gapped include:

Full isolation: Deploy Teradata in an air-gapped environment with no external connectivity— keeping classified and regulated data fully containedSecurity and compliance: Meet stringent regulatory requirements while maintaining complete audit trails and governanceHigh-performance analytics and AI at scale: Run complex queries across billions of rows and petabytes of data with predictable performance

Why it Matters
Research points to growing urgency. According to a 2026 BARC global study of 320 business and IT decision-makers, 62 percent of organizations cite data and AI in core business processes as a key driver of sovereignty requirements. A separate IBM study found that 91 percent of executives say they do not fully understand their organization’s dependencies across AI vendors, models and infrastructure. And Gartner predicts that by 2030, more than 75 percent of enterprises outside the United States will have a digital sovereignty strategy, supported by a sovereign cloud strategy.

As AI moves into mission-critical operations, sovereignty is no longer only about where data resides. Organizations must also consider where AI runs, which models are used, how enterprise information is accessed, who operates the environment, and which external technologies their AI depends on.

This is particularly relevant for governments, defense organizations, regulated industries and critical infrastructure, where data residency, privacy, jurisdiction, operational continuity or national-security requirements can limit the use of traditional public-cloud AI services.

Teradata frames sovereignty across three dimensions: data sovereignty (where data lives and who can access it), operational sovereignty (who controls the environment and whether it can operate independently), and digital sovereignty (which technologies and AI models the organization depends on). An organization can satisfy data residency requirements and still lack full sovereignty if AI processing occurs externally, external administrators can access the environment, or operations stop when public connectivity is unavailable.

Teradata’s sovereign portfolio is designed to address these requirements, bringing enterprise analytics and AI inside the boundaries that organizations control.

The Deployment Portfolio
Teradata Factory and Teradata Sovereign Cloud on Google Distributed Cloud air-gapped address different sovereignty requirements.

Teradata Factory is an integrated hardware and software rack that brings a private enterprise data and AI environment into customer-controlled on-premises infrastructure. It is designed for organizations that require direct physical control over their infrastructure and AI execution.

Teradata Sovereign Cloud on Google Distributed Cloud air-gapped provides a cloud operating model inside a sovereign boundary. The environment has no connection to the public internet and is operated by the customer. It is designed for organizations that need the operational model of cloud within a fully isolated, sovereign environment. Teradata provides the enterprise analytics layer within the air-gapped boundary, enabling organizations to support use cases such as operational reporting, mission analysis, intelligence fusion, training and simulation analytics, and defense logistics and sustainment inside the sovereign boundary.

Together, Teradata Factory and Teradata Sovereign Cloud on Google Distributed Cloud air-gapped support a hybrid approach to sovereign deployment, giving organizations the flexibility to run enterprise analytics and AI inside whichever boundary their requirements demand.

Executive Perspectives
“As AI becomes embedded in mission-critical decisions, organizations can no longer treat sovereignty as a data-location requirement. They need control over where AI runs, which models are used, who can operate the environment, and whether critical workloads can continue if external connectivity disappears. And increasingly, sovereignty is not a single location. It is an architecture that spans public cloud, sovereign cloud, and on-premises infrastructure. Teradata is uniquely built for that reality. We can run in all of those environments, connect them through Teradata Fabric without requiring data to move, and give organizations the freedom to choose the AI models and infrastructure that work for them.”

— Sumeet Arora, Chief Product Officer, Teradata

“Google Distributed Cloud is purpose-built, air-gapped infrastructure that delivers complete technical, operational and software sovereignty. By combining Google Distributed Cloud’s sovereign infrastructure with Teradata’s AI and knowledge capabilities, we are giving enterprise customers in regulated industries the ability to innovate without sacrificing control over their data.”

— Muninder Singh Sambi, VP of Product Management, Google Distributed Cloud

Availability
The expanded Teradata sovereign capabilities for Teradata Factory with AI Studio will be available in Q1 2027. Teradata Sovereign Cloud on Google Distributed Cloud air-gapped is available in Private Preview.

About Teradata
Teradata empowers enterprises to turn intelligence into autonomous action, grounding AI agents in deep business context and trusted data. As AI agents multiply, Teradata is the context foundation, governance layer, and performance backbone that companies need now. The Teradata Autonomous Knowledge Platform puts AI into production across cloud, on-premises, and hybrid environments.

Teradata Forward-Looking Statements
This press release contains “forward-looking statements” about the expectations, beliefs, plans, and intentions relating to Teradata’s sovereign portfolio. Such statements include statements regarding future product availability, capabilities, and offerings, and expected benefits to Teradata customers. Forward-looking statements are subject to known and unknown risks and uncertainties and are based on potentially inaccurate assumptions that could cause actual results to differ materially from those expected or implied by the forward-looking statements. If any such risks or uncertainties materialize or if any of the assumptions prove incorrect, Teradata’s results could differ materially from the results expressed or implied by the forward-looking statements made. Teradata undertakes no obligation, and does not intend to update the forward-looking statements. Factors that may cause actual results to differ materially from those in any forward-looking statements include: (i) delays and unexpected difficulties and expenses in executing the planned product capabilities and offerings, (ii) changes in the regulatory landscape related to AI and (iii) uncertainty as to whether customer adoption will justify the investments in the product capabilities and offerings. Further information on factors that could affect Teradata’s financial and other results is included in the filings Teradata makes with the Securities and Exchange Commission from time to time.

The Teradata logo is a trademark, and Teradata is a registered trademark of Teradata Corporation and/or its affiliates in the U.S. and worldwide.

MEDIA CONTACT
January Machold
January.Machold@Teradata.com

View original content:https://www.prnewswire.com/news-releases/teradata-expands-sovereign-ai-across-on-premises-and-air-gapped-cloud-environments-302913587.html

SOURCE Teradata Corporation

Drivefulness™ research explores the human side of a nearly $100 billion roadway risk

BLOOMINGTON, Ill., Oct. 20, 2026 /PRNewswire/ — As vehicles become more connected and technology-enabled, the challenge of distracted driving is becoming more complex across the country. The question is no longer simply what distracts drivers, but understanding why people make certain choices behind the wheel and how technology may influence those decisions.

State Farm® and the MIT AgeLab are exploring those questions through a research initiative examining driver attention, distraction, and the behaviors that influence decision-making behind the wheel. Researchers have coined the term “drivefulness” to describe a state in which a driver is safe, aware, appropriately engaged, and ready to respond to the road, while being supported in having the best possible driving experience for them.

The study brings together State Farm’s decades of experience understanding crash risk, driver behavior, and the real-world consequences of distraction with MIT AWARE initiative’s multidisciplinary research expertise in transportation safety, human factors, behavioral science, and how people interact with technology.

Distracted driving carries significant human and economic consequences. According to U.S. Department of Transportation research, distraction was involved in 29% of motor vehicle crashes studied, contributing to more than 10,000 deaths, 1.3 million injuries, and an estimated $98.2 billion in economic costs.

Understanding the relationship among driver attention, distraction, and behaviors could help inform how automakers, organizations, regulators, and insurers approach roadway safety, risk prevention, and the design of future driving experiences.

“Distracted driving is not simply about whether a driver knows what is safe or unsafe. It reflects a much more complex interaction between the driver’s state, the demands of the driving environment, and the technologies that compete for or support their attention,” said Dr. Pnina Gershon of the MIT AgeLab. “With this research, we are looking beyond individual moments of distraction to understand what drivers need to be prepared, attentive, and appropriately engaged during the driving task. That allows us to think differently about safety and about how technology, information, and interventions can support drivers before, during, and after the drive.”

Dr. Gershon is the director of the MIT AWARE initiative and a co-director of the MIT Advanced Vehicle Technology Consortium. With more than two decades of research experience, she leads work at the intersection of driving safety, human behavior, health, and human-centered AI. Her research uses sensing, real-world data, and behavioral analytics to understand human states and to develop technologies and interventions that support safer, more effective interactions between people and increasingly automated systems.

Understanding the behavior behind the risk

Drivers may know distraction is dangerous, but State Farm research shows that knowledge doesn’t always change what happens behind the wheel. Sixty-eight percent of drivers admit to engaging in distracting behaviors while driving, and 44% say they have crashed or experienced a near-crash while doing so.

Perception may be part of the challenge. Sixty-two percent of drivers believe taking their eyes off the road for two or more seconds is safe, even though a two-second glance away doubles crash risk.

“Technology is changing the driving experience, creating new opportunities to better understand and support drivers,” said Jeff Legner, technology executive at State Farm. “The more we learn about how people interact with technology behind the wheel, the better positioned we are to anticipate evolving risks, improve driver experiences and explore new approaches to accident prevention. This research can help turn those insights into meaningful ways to support drivers before, during and after every drive.”

Combining real-world risk with behavioral science

State Farm has worked to address distracted driving from multiple angles, including advocating for stronger hands-free laws, supporting cross-industry safety research and advancing technology that can help drivers better understand their behavior. That work includes telematics-based tools for customers and collaboration with companies such as Cambridge Mobile Telematics to explore how technology and data can help reduce roadway risk. The MIT AgeLab brings deep expertise in understanding the human side of a rapidly changing driving environment.

The collaboration builds on an established research relationship between State Farm and MIT, including State Farm’s participation in the MIT Advanced Vehicle Technology Consortium, which studies how drivers interact with increasingly automated and technology-enabled vehicles.

First findings expected in October
The first phase of the research is focused on establishing a deeper understanding of how people perceive and manage attention behind the wheel.

Findings from the first phase of the drivefulness study are expected later in October 2026, providing an early look at what the research is uncovering about driver attention, distraction and behavior through consumer survey findings.

The findings will help inform the next phase of drivefulness research, beginning in January 2027, on how people, technology and the driving environment can work together to support safer driving.

About State Farm®
For over 100 years, the mission of State Farm has been to help people manage the risks of everyday life, recover from the unexpected and realize their dreams. State Farm Mutual Automobile Insurance Company and its affiliates are the largest providers of auto and home insurance combined in the United States. Its more than 19,200 agent offices and over 62,000 employees serve over 96 million policies and accounts – including auto, fire, life, health, commercial policies and financial services accounts. Commercial auto insurance, along with coverage for renters, business owners, boats and motorcycles, is also available. State Farm Mutual Automobile Insurance Company is the parent of the State Farm family of companies. State Farm is ranked No. 32 on the 2025 Fortune 500 list of largest companies. For more information, please visit http://www.statefarm.com.

About the AWARE initiative at the MIT AgeLab
AWARE is an MIT research initiative focused on developing intelligent vehicle systems that can better understand drivers and support them in real-world conditions. Based at the MIT AgeLab and Center for Transportation & Logistics, AWARE brings together naturalistic driving research, advanced simulation, behavioral science, and artificial intelligence to study how driver state, behavior, and context interact across the driving journey. Its research examines areas such as distraction, fatigue, impairment, workload, automation use, driver monitoring, and human-machine interaction, with the goal of moving beyond simply detecting driver behavior toward systems that can sense, interpret, respond, and promote safer and more appropriate driver engagement. Through this work, AWARE is building the evidence and methods needed to support the next generation of adaptive, human-centered mobility technologies. For more information about AWARE, visit https://ctl.mit.edu/aware-initiative, and about the AgeLab, visit https://agelab.mit.edu/.

View original content to download multimedia:https://www.prnewswire.com/news-releases/state-farm-and-mit-agelab-collaborate-to-study-whats-driving-distraction-behind-the-wheel-302913331.html

SOURCE State Farm