New EMA research finds that 82% of IT and security leaders have experienced AI deployment delays due to security or compliance reviews, while organizations are limiting agent capabilities to accelerate adoption

LAFAYETTE, Colo., Oct. 20, 2026 /PRNewswire/ — Enterprise Management Associates (EMA™), a leading IT research and consulting firm, today announced The State of AI Friction: Delayed, Diminished, and Ungoverned AI, a live webinar examining the security and governance challenges slowing enterprise AI adoption and limiting its potential. The webinar will feature Christopher M. Steffen, VP of Research covering information security, risk, and compliance management at EMA, and James Rice, VP of Product Marketing and Strategy at Protegrity.

In a study conducted for Protegrity, EMA surveyed 152 IT and security leaders and found that 82% have experienced security or compliance reviews delaying AI deployment. Two-thirds of these delays last a month or longer, creating a growing challenge for organizations trying to move AI initiatives from experimentation to production while keeping pace with rapid advances in AI capabilities.

The pressure to deploy AI faster creates a second challenge: organizations may be limiting AI’s capabilities or operating agents without adequate safeguards. The study found that 81% have deployed AI agents with tools disabled or autonomy restricted to accelerate production deployment. At the same time, 69% already run agents that write to databases and trigger external APIs, introducing risks that traditional data security controls may not adequately address.

Encryption, data masking, and role-based access remain important safeguards, but organizations must also account for how autonomous agents access data, execute actions, and interact with enterprise systems. As organizations expand their AI initiatives, the gap between agent capabilities and existing security and governance controls becomes increasingly difficult to ignore. With 88% of respondents planning to increase agentic AI budgets by at least 10% next year, addressing this gap will be critical to realizing the value of those investments.

“Security teams aren’t trying to slow AI down, but the review processes most organizations rely on weren’t built for systems that act on their own. When the choice comes down to waiting months or switching off the capabilities that make agents valuable, nobody wins,” said Steffen. “Organizations need security and governance controls that move at the speed of AI, not the speed of a review cycle.”

During the webinar, Steffen and Rice will examine the research findings and discuss:

The business impact of AI delays: How extended security reviews affect deployment timelines and the ability to keep pace with a rapidly evolving AI landscape.The hidden cost of constrained AI: Why limiting agent tools and autonomy can undermine the value organizations expect from their AI investments.The security gap surrounding AI agents: Where traditional data protection approaches fall short as agents access sensitive information and take actions across enterprise systems.A path to secure AI in production: EMA’s recommendations for aligning security and governance controls with the capabilities and risks of agentic AI.

The webinar will take place Tuesday, October 27, 2026 at 1:00 p.m. Eastern. Registration is available at https://info.enterprisemanagement.com/the-state-of-ai-friction-webinar-pr

About EMA 
Founded in 1996, EMA is a leading IT research and consulting firm dedicated to delivering actionable insights across the evolving technology landscape. Through independent research, market analysis, and vendor evaluations, we empower organizations to make well-informed technology decisions. Our team of analysts combines practical experience with a deep understanding of industry best practices and emerging vendor solutions to help clients achieve their strategic objectives. Learn more about EMA research, analysis, and consulting services at www.enterprisemanagement.com and follow them on X and LinkedIn.

Media Contact:
Raleigh Gould
Enterprise Management Associates
rgould@enterprisemanagement.com

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SOURCE Enterprise Management Associates

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

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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

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SOURCE Teradata Corporation