In Cloud Infrastructure news this week…

AWS Enhances Site-to-Site VPN with Flexible Bandwidth Modification Capabilities
Amazon Web Services has introduced enhanced bandwidth modification capabilities for its Site-to-Site VPN service. The update enables enterprises to dynamically adjust connectivity performance based on operational needs, improving flexibility and scalability. As hybrid and distributed work environments expand, adaptable cloud networking solutions are becoming increasingly important.

In Business Technology news this week…

CLA Launches CLA One Digital to Drive Business-Focused Digital Transformation
CLA has launched CLA One Digital, a platform designed to support business-centric digital transformation initiatives. The offering combines advisory expertise with technology services to help organizations modernize operations and improve decision-making. This reflects the growing convergence of consulting and digital innovation strategies.

In Cloud Security & AI news this week…

OpenAI Introduces Advanced Account Security Features to Advance User Protection
OpenAI has rolled out advanced account security features aimed at strengthening user protection across its AI platforms. The enhancements focus on authentication, monitoring, and threat prevention to safeguard enterprise and consumer interactions. As AI systems become more deeply integrated into workflows, robust security measures are critical for trust and adoption.

In Artificial Intelligence news this week…

AWS Introduces Generative AI Model Agility Solution
Amazon Web Services has launched a generative AI model agility solution to help enterprises manage and switch between AI models more efficiently. The platform is designed to reduce vendor lock-in and enable organizations to optimize performance across diverse AI workloads. This highlights the increasing need for flexibility in enterprise AI strategies.

In Quantum Computing news this week…

IBM Marks a Decade of Quantum Computing on the Cloud, Driving Global Innovation
IBM is celebrating ten years of cloud-based quantum computing innovation. Over the past decade, the company has expanded access to quantum systems for researchers, developers, and enterprises worldwide. This milestone reflects the growing maturity of quantum computing and its long-term potential across industries.

In FinTech news this week…

Anthropic Expands into Finance with AI Agents Built for Banking and Enterprise Operations
Anthropic is expanding into financial services with AI agents designed for banking and enterprise operations. These AI systems aim to automate workflows, enhance customer interactions, and improve operational efficiency in regulated environments. The move signals the rapid adoption of AI agents across the financial sector.

In HealthTech & Analytics news this week…

Ibex Medical Analytics Establishes Biopharma Advisory Board to Advance AI-Powered Precision Diagnostics
Ibex Medical Analytics has formed a biopharma advisory board to support the advancement of AI-powered precision diagnostics. The initiative aims to strengthen collaboration between healthcare and pharmaceutical stakeholders to improve diagnostic accuracy and accelerate personalized medicine efforts.

In IT Operations & Automation news this week…

Kyndryl Launches Agentic AI Capability to Prevent IT Outages and Accelerate Recovery
Kyndryl has introduced a new agentic AI capability designed to proactively prevent IT outages and speed up recovery processes. By leveraging autonomous AI agents, the platform can identify risks, automate remediation, and improve operational resilience. This reflects the growing trend toward self-healing IT environments.

Article of the Week

How Enterprises Are Using AI Agents to Run End-to-End Business Processes

AI agents are rapidly evolving from task-specific assistants into systems capable of managing end-to-end business processes. Enterprises are using these intelligent agents to automate workflows, coordinate operations, and improve decision-making across departments. As AI agents become more autonomous and context-aware, they are reshaping enterprise operations by reducing manual effort, increasing efficiency, and enabling scalable digital transformation.

The post ITDigest’s Weekly News Roundup Featuring AWS, CLA, OpenAI, IBM, Anthropic, Ibex and Kyndryl appeared first on ITDigest.

For instance, these rapidly advancing AI features will allow AI agents not only to interact with customers and to perform routine tasks but also to access and manipulate business-critical content in a completely automated way. This new kind of AI can offer tremendous leverage in productivity but will also generate types of cyber risks that conventional security models have no answers for.

Cognizant said its new service focuses on establishing “provable trust” in AI systems through continuous monitoring and evidence-based assurance. Rather than relying on traditional static defenses, the platform is built to secure AI systems during both development and real-world deployment. The company noted that modern AI systems are probabilistic and context-aware, making them vulnerable to threats such as manipulated prompts, poisoned models, and compromised agent behavior.

Also Read: Transflo Unveils ‘Workflow AI for Factors’ to Modernize and Accelerate the Factoring 

“AI is fundamentally changing how enterprise systems behave,” said Vishal Salvi, Global Head of Cognizant’s Cybersecurity Service Line. “These systems are adaptive, context-driven and increasingly autonomous – and securing them requires continuous assurance across build and run-time environments. With Cognizant Secure AI Services, we are helping enterprises engineer trust into AI systems from day one and to sustain that trust as those systems evolve.”

The offering combines three major components: a secure Agent Development Lifecycle (ADLC) framework, Cognizant Neuro® Cybersecurity for unified threat monitoring and audit support, and Cognizant Trust, which provides policy enforcement, traceability, and responsible AI governance.

All these features together aim to cover the entire operational lifecycle for AI model security, data protection, identity management, AI DevOps security, and generative AI risk mitigation.

Based on Cognizant, they are currently helping over 250 companies in regulated industries to carry out AI security and digital transformation projects. Some of the first implementation examples include protection against deepfake fraud, model tampering, and risks related to autonomous AI agents operating within enterprise systems.

Research analysts working in the industry point out that businesses are looking more and more for integrated AI security systems that not only cover development but also operational environments. With the rapid spread of AI, there is a rising need for governance models that are capable of securing complex AI ecosystems while at the same time allowing for compliance and transparency.

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Built natively within the ServiceNow ecosystem, SmartAI Ops is intended to help organizations monitor and manage AI deployments in real time. While AI Control Tower establishes governance frameworks, policies, and orchestration for AI systems, SmartAI Ops focuses on operational oversight — offering insights into AI spending, workforce productivity, Model Context Protocol (MCP) tool usage, and system alerts.

The platform arrives as enterprises accelerate adoption of agentic AI technologies and face growing pressure to understand the financial and operational impact of these deployments. Organizations often struggle with fragmented visibility across AI providers, unclear ROI metrics, and limited oversight into AI tool interactions and connector failures.

Also Read: Malwarebytes Integrates Real-Time Scam Detection into Claude AI Conversations

SmartAI Ops addresses these challenges through five dedicated ServiceNow workspaces covering AI economics, developer productivity, user engagement, MCP observability, and operational alerts. The solution enables teams to track AI-related costs by system and provider, monitor usage trends, and proactively identify anomalies before they escalate into larger operational or budget issues.

“AI Control Tower is one of the most important enterprise software innovations of our generation. What our customers tell us is that alongside those rules and rails, they need complementary visibility into what AI is costing them, whether their teams are getting real value, and where risks are forming before they become problems. SmartAI Ops delivers exactly that, as a native ServiceNow workspace built to sit right alongside AI Control Tower and make every governance investment more powerful. Being named the 2026 AMS Breakthrough Transformation Partner of the Year at Knowledge is the perfect moment to put this in the hands of every enterprise betting on ServiceNow AI,” said Tom Thomson, CEO of C1Secure.

The launch aligns with ServiceNow’s broader push into agentic AI capabilities, including advancements around Build Agent, Fluent workflows, and expanded AI Control Tower functionality. As AI agents increasingly rely on MCP servers and automated workflows, operational transparency is becoming a critical requirement for enterprise governance and compliance.

The post C1Secure Introduces SmartAI Ops to Strengthen Enterprise AI Visibility on ServiceNow appeared first on ITDigest.

Cutting Out the Friction from the “Cab-to-Cash” Process

Historically, factoring has been characterized by paper-based documents and inefficient communications. With an advanced architectural design that places users at the forefront, the new Workflow AI for Factors solution streamlines the entire workflow, including document ingestion, processing, approval, and financing.

As a neutral technology provider, Transflo ensures that this increased efficiency does not come at the cost of data security or relationship integrity.

“Our goal was simple: remove complexity from factoring,” said Renee Krug, CEO of Transflo. “We’ve combined automation, intelligent workflows, and a clean user experience to create a platform that is faster, more accurate, and significantly easier to use. This is a meaningful step forward for our customers and the industry.”

Also Read: Operant AI Introduces Endpoint Protector to Address Enterprise AI Security Blind Spots

“Our customers value confidentiality and trust,” added Krug. “Because Transflo is a neutral technology partner, our customers can confidently run their operations on our platform, knowing their data is protected and their business relationships stay intact.”

A Smarter, Scalable Factoring Engine

Workflow AI for Factors introduces a suite of intelligent capabilities designed for high-velocity financial operations:

End-to-End Automation: Custom AI agents carry out document capturing and data extraction, leading to greatly decreased manual input.

User-friendly Interface: An intuitive UI enables employees to process high volumes of transactions with no need for extensive training.

Built-in Validation Engine: A powerful built-in engine verifies invoices’ data in real-time, thus minimizing the chances of fraud or mistakes happening during the process.

Quick Payment Approval: The automation of the validation process makes it possible for factors to make decisions regarding funding in record time.

Scalability for Enterprises: The system is capable of processing numerous transactions in compliance with the high-performance standards of the current transportation industry.

“This platform is about efficiency and confidence and speed of funding,” said Bill Vitti, President and Chief Revenue Officer of Transflo. “Customers can trust that transactions are verified, accurate, and processed quickly, without the delays and errors that have historically impacted the industry.”

Live Demonstrations at IFA 2026

The launch coincides with the International Factoring Association (IFA) 2026 Conference in Nashville. Attendees can visit the Transflo team at Booth #12 from May 6-8 to experience live walkthroughs of the Workflow AI platform and see how it is redefining speed and precision in freight factoring.

The post Transflo Unveils ‘Workflow AI for Factors’ to Modernize and Accelerate the Factoring appeared first on ITDigest.

Governing AI agents is critical, now more than ever, as enterprises accelerate their deployment of agentic technology across systems, teams, and tools at unprecedented speed. Realizing the full value of this investment requires a unified approach to governance, identity and permission management, and control that spans every platform across which AI agents operate. ServiceNow is working with partners like Microsoft to give enterprises the visibility to see AI agents, models, tools, and prompts in their environment, which gives them the control to govern them consistently and the interoperability to put them to work across the tools employees already use.

“ServiceNow and Microsoft are helping organizations maximize value from every AI investment,” said Jon Sigler, executive vice president and general manager, AI Platform at ServiceNow. “With this expanded integration, customers can securely apply governance across ServiceNow and Microsoft environments with integrated visibility and controls, while putting ServiceNow Autonomous Workforce to work across the Microsoft 365 environment. This is an example of what it means to put AI to work at enterprise scale, with the trust and interoperability that business transformation requires.”

Also Read: Wrike Connects Governed AI To Enterprise Workflows, Now Available On OpenAI GPT Store

“One of the most important things we can do for enterprises is bring intelligence and action together in a secure, connected way,” said Charles Lamanna, executive vice president, Copilot, Agents, and Platform at Microsoft. “That’s why we’re excited to collaborate with ServiceNow, bringing their AI expertise into Microsoft 365 to add workflow intelligence on top of that secure foundation. Together, we’re helping customers act on insights more quickly and drive meaningful outcomes across their business processes.”

Unified control for a multi-agent enterprise

ServiceNow AI Control Tower already connects to Microsoft Foundry and Copilot Studio to discover AI assets, enforce governance policies, and drive consistent oversight. The new AI Control Tower integration with Microsoft Agent 365 expands these capabilities by extending visibility and governance insights across Microsoft Agent 365’s AI agent ecosystem. This gives IT and operations teams enhanced visibility into agent activity across ServiceNow and Microsoft ecosystems, regardless of where these agents were built or deployed.

Through the integration, ServiceNow AI Control Tower gives administrators the ability to review and approve ServiceNow AI specialists prior to submission to the Microsoft Agent 365 Marketplace, where Microsoft publishing and policy controls apply. This helps ensure that every ServiceNow AI specialist that interoperates with the Microsoft 365 environment has been vetted, permissioned, and authorized for deployment.

ServiceNow AI specialists come to work across Microsoft 365 applications

In the Microsoft Agent 365 Marketplace, a ServiceNow AI specialist will appear in the org chart as a digital employee with defined roles, permissions, and accountability.

This allows the AI specialists to be able to take actions such as drafting a Word document, responding to email messages in Outlook, or acting on an assigned comment in PowerPoint, subject to Microsoft 365 permissions, identify, and admin policy controls, while consumption is tracked across both ServiceNow’s and Microsoft’s metered usage models.

SOURCE: Businesswire

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The partnership focuses on embedding AI agents across core financial workflows, including planning, forecasting, procurement, payments, treasury, tax, and reporting. These AI systems are designed to automate repetitive processes, generate insights, and enhance decision-making while maintaining governance and control through human supervision.

Unlike traditional automation tools, the AI agents being developed through this collaboration are intended to operate dynamically across systems, learning and improving over time. The initiative is also grounded in real-world implementation, with early use cases being tested within OpenAI’s own finance operations before being scaled to enterprise clients.

At its core, the collaboration reflects a broader industry trend: the transition from experimental AI deployments to fully integrated, enterprise-wide systems. By combining PwC’s domain expertise in finance and consulting with OpenAI’s advanced AI capabilities, the initiative aims to redefine the “office of the CFO” into a more strategic, data-driven function capable of delivering predictive insights rather than just historical reporting.

What does this mean for the fintech industry?

The implications for the fintech sector are profound. Fintech companies, which already operate at the intersection of finance and technology, are likely to accelerate their adoption of agentic AI models to remain competitive. This development sets a new benchmark for operational efficiency, where AI is no longer a supporting tool but a core component of financial infrastructure.

First, the introduction of AI-native finance functions could significantly reduce operational costs by automating labor-intensive processes such as reconciliation, compliance checks, and financial reporting. This enables fintech firms to allocate resources toward innovation and customer-centric services rather than back-office functions.

Second, the ability of AI agents to continuously analyze large datasets in real time will enhance risk management and fraud detection capabilities. Financial institutions can move from reactive to proactive strategies, identifying anomalies and potential risks before they escalate. This aligns with broader industry efforts to strengthen governance and improve decision-making through AI-driven insights.

Third, the collaboration highlights a shift toward “agent ecosystems,” where multiple AI systems work together seamlessly across business functions. For fintech companies, this could lead to the development of more integrated platforms that combine payments, lending, compliance, and analytics into unified solutions, ultimately improving customer experiences and operational agility.

Also Read: Customers Bank and OpenAI Partner to Build an AI-Native Commercial Banking Model 

Beyond fintech, the impact on businesses across industries is equally significant. Organizations that adopt AI-native finance functions can expect faster financial close cycles, more accurate forecasting, and improved strategic planning. This transformation also redefines the role of finance professionals, shifting their focus from manual processing to higher-value activities such as analysis, strategy, and innovation.

However, the transition is not without challenges. Companies must address concerns related to data security, regulatory compliance, and ethical AI use. The emphasis on human oversight in the PwC-OpenAI model underscores the importance of maintaining accountability and transparency as AI systems become more autonomous.

In the long term, this collaboration may serve as a blueprint for how enterprises integrate AI into critical business functions. As AI adoption continues to scale, companies that successfully combine technology with governance and human expertise are likely to capture a disproportionate share of the value generated by AI.

Ultimately, the PwC and OpenAI partnership represents more than just a technological advancement it signals a fundamental shift in how financial operations are designed and executed. For the fintech industry and beyond, the message is clear: the future of finance will be AI-native, adaptive, and deeply integrated into the fabric of enterprise decision-making.

The post PwC and OpenAI Partner to Build AI-Native Finance Function, Reshaping the Future of Fintech appeared first on ITDigest.

Also Read: OpenAI and Microsoft Redefine Partnership for the Multi-Cloud AI Era

By combining Anthropic’s advanced AI capabilities with the financial strength and extensive enterprise networks of its partners, the initiative seeks to create a scalable and repeatable model for enterprise AI deployment. Industry observers note that the collaboration reflects a broader shift in the AI ecosystem, where technology companies and financial institutions are increasingly joining forces to accelerate commercialization and adoption. The venture is also positioned to compete with traditional consulting firms by offering AI-first services that directly embed intelligent systems into business processes rather than relying solely on advisory models. With significant capital backing reportedly around $1.5 billion and access to a vast portfolio of companies, the new entity aims to rapidly expand AI integration across industries, driving operational efficiency and innovation at scale. Ultimately, this partnership underscores the growing importance of enterprise AI as a key driver of competitive advantage and signals a new phase in the evolution of generative AI, where deployment, customization, and measurable business outcomes take center stage.

Read More: Anthropic Partners with Blackstone, Hellman & Friedman, and Goldman Sachs to Launch Enterprise AI Services Firm

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Durham-Kilcullen joins from outside the ServiceNow ecosystem, bringing more than a decade of experience as an engineer and data scientist across regulated industries, including healthcare, higher education, and financial services.

As Chief AI Officer, he will shape how Ondaro applies AI internally and for clients—guiding strategy, implementation, and scale, while influencing the company’s go-to-market approach.

“Joining a team already at the forefront of enterprise transformation on ServiceNow at the exact moment AI is redefining what that transformation looks like is a rare opportunity,” said Durham-Kilcullen. “Too many organizations are stuck in pilot mode. We have the opportunity to help customers move beyond experimentation and become truly AI-driven. I’m excited to build on Ondaro’s deep platform expertise to deliver real, lasting impact from day one.”

This appointment comes as organizations shift from early AI experimentation to enterprise-wide adoption—seeking clearer paths to scale, stronger governance, and measurable outcomes.

Also Read: Runloop Launches Benchmark Orchestration Platform to Enable Trusted AI Agent Deployment

Most recently, Durham-Kilcullen served as Chief Innovation Officer at Stova, a leading event management technology platform, where he launched the company’s enterprise data infrastructure and advanced its AI capabilities. He also founded Kilcullen Technologies, advising organizations on modern architecture, scalable cloud systems, and AI/ML strategy.

“Adding a technologist of Ben’s caliber to our executive team comes at a pivotal moment as AI reshapes how work gets done,” said Jeff Gregory, CEO of Ondaro. “He brings a rare combination of technical depth and outcome-driven thinking that aligns directly with how we serve our clients. We’re thrilled to welcome him as we continue helping organizations navigate what’s next.”

Gregory adds: “Many firms are layering AI onto a decade-old delivery playbook and calling it transformation. It’s not. Our clients have moved past use cases and are asking for operating models, governance, and demonstrable value. We’ve already been building those solutions, and Ben joining allows Ondaro to scale and expand them. He’s done this in healthcare, financial services, and higher education, where the bar for governance and outcomes is highest. That’s the wave our clients are paddling into now and we’re already riding it.”

With this addition, Ondaro is accelerating its investment in practical AI—equipping its teams to work more efficiently while delivering stronger, more measurable outcomes for clients.

Source: BusinessWire

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Addressing this “lock-in” anxiety, Amazon Web Services (AWS) recently introduced the Generative AI Model Agility Solution. This comprehensive framework and toolset are designed to help enterprises migrate and swap LLMs within their production environments seamlessly. By decoupling the application logic from the specific model backend, AWS is providing the industry with a roadmap for long-term sustainability in AI deployment.

A Design for Model-Agnostic AI

The AWS Generative AI Model Agility Solution is not merely an individual piece of technology; it is a proven set of techniques to construct “agile” AI frameworks. As explained in the press release, the solution tackles all technical roadblocks that generally render the process of changing models impossible-such as disparities in prompt engineering, output formatting, and API.

Some of the main elements of the solution are:

Prompt Management Standardization: Software to centralize and version prompts, ensuring that they are easily adaptable in case the user switches the model from Claude to Llama or Titan.

Universal API Gateway: Using Amazon Bedrock, the solution offers a unified interface for several models, enabling users to swap the foundational LLM with only a minor tweak of their configuration files, without having to modify their code entirely.

Model Evaluation Systems: Software integrations such as Model Evaluation on Amazon Bedrock to assess how the “new” model stacks up against the “old” model in terms of performance, bias, and accuracy.

Also Read: Circles and OpenAI Advance AI-Native Telco Vision with New Intelligent Concierge Milestone

Governance and Token Expense Oversight: Advanced analytics to guarantee that changing models does not result in unwanted surges in token expenses or data protection infringements.

Impact on the Artificial Intelligence Industry

This move by AWS signals a maturation of the Artificial Intelligence sector, moving from the “hype phase” to the “industrialization phase.”

1. The End of Model Monopoly For the past two years, much of the industry’s power was concentrated in the hands of the few organizations that could build the most capable frontier models. However, as “model agility” becomes a standard practice, the power shifts back to the infrastructure and application layers. If models are interchangeable, the value lies in the data, the workflow, and the user experience-not just the weight of the neural network.

2. Accelerating the “Race to the Bottom” for Pricing When businesses can easily switch models, LLM providers are forced to compete more aggressively on price and performance. AWS’s solution facilitates a “plug-and-play” market where the most efficient model wins. This transparency will likely accelerate the commoditization of base-layer LLMs, driving down costs for the entire ecosystem.

3. Standardization of “AI Middleware” The Model Agility Solution establishes a need for a “middleware” layer in the AI stack. Just as SQL standardized how applications talk to databases, tools like Bedrock and the Model Agility framework are standardizing how applications talk to intelligence. This creates a massive secondary market for AI orchestration and observability tools.

Effects on Businesses Operating in the Industry

For enterprises-from tech startups to traditional corporations-the ability to maintain “model agility” is a critical business continuity requirement.

Risk Mitigation: For a business, being dependent on a single AI provider is a significant operational risk. If a provider changes their terms of service, experiences downtime, or fails to keep up with safety standards, an “agile” business can pivot in hours rather than months.

Cost Optimization: Different tasks require different levels of intelligence. A customer support bot might need a cheap, fast model for basic queries but a “frontier” model for complex troubleshooting. Model agility allows businesses to route tasks to the most cost-effective model in real-time, significantly improving margins.

Reduced “Technical Debt”: Many companies have hesitated to go into full production for fear of building on a “dead-end” technology. AWS’s framework provides the confidence to build now, knowing that the architecture can evolve as the AI field advances.

Faster Time-to-Market: By using standardized templates and gateways, development teams can spend less time on backend integrations and more time on the unique value proposition of their AI products.

Conclusion

The AWS Generative AI Model Agility Solution marks a significant departure into a future that embraces openness, competition, and resilience within the world of artificial intelligence. With the capability to create interchangeable LLMs, AWS is eliminating one of the key deterrents in the uptake of AI applications by businesses—the risk of obsolescence. As the “Agentic Web” and self-governing processes become the norm, having the capacity to change the “brain” of the application without altering its “body” will define success in any AI-enabled organization.

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Founded by researchers from UC Berkeley’s RISELab, and now with this acquisition, OPAQUE supports Confidential AI workflows across training and inference, moving enterprises from isolated AI experiments to production deployment 4-5x faster.

The acquisition was overseen by H.E. Faisal Al Bannai, Adviser to UAE President His Highness Sheikh Mohamed bin Zayed Al Nahyan and Secretary General of ATRC, and Ion Stoica, Co-Founder and Board Member of OPAQUE, Co-Founder of Databricks, and Professor of Computer Science at UC Berkeley. It marks the first time cryptographic AI technologies developed in the UAE have been acquired and deployed at scale by a US-based technology company.

Also Read: Sapio Sciences Brings Claude Cowork to the Lab

“This acquisition reflects what the UAE has set out to do – produce foundational technology that the world adopts,” said H.E. Faisal Al Bannai, Adviser to the UAE President and Secretary General of ATRC. “Cryptographic AI capabilities developed in Abu Dhabi are now being deployed at global scale by a leading US technology company. The UAE is not only adopting the AI economy – we are leading. ATRC will continue to invest in the research that defines the next generation of trusted AI.”

“The future of AI depends on unlocking the data organizations have never been able to touch,” said Ion Stoica, Co-Founder of OPAQUE. “Most enterprises sit on a corpus of data too sensitive to use and too valuable to ignore. With this acquisition, OPAQUE is the only platform delivering hardware-attested cryptographic evidence across the full AI lifecycle – training, fine-tuning, inference, and agents – with protections engineered to withstand quantum-era threats. That combination doesn’t exist anywhere else in the market today.”

“AI agents are extraordinarily powerful: they operate at machine speed with human-like capabilities, delivering in days what entire teams would struggle to complete in a year,” said Aaron Fulkerson, Chief Executive Officer of OPAQUE. “That same power is exactly why they are so difficult to move into production. An agent connected to sensitive systems or regulated data — patient records, clinical research, financial claims – can do damage in days that a team of malicious actors couldn’t match in a year. The only way to safely deploy them is with hardware-enforced, verifiable rules: provable evidence of what ran, where it ran, and which rules were enforced. Building on primitives from every major cloud provider, OPAQUE makes it possible to move agents into production – even on the most sensitive and most regulated systems and data.”

Enterprises sit on vast amounts of sensitive data — patient records, financial transactions, classified intelligence, proprietary research – that could transform their AI capabilities. But using that data across the full AI lifecycle has required stitching together point solutions from multiple vendors, each covering a different phase: one for training, another for inference, another for agent workflows. The result is gaps, complexity, and risk that compliance teams won’t sign off on.

OPAQUE eliminates those gaps. The acquired technology extends OPAQUE’s confidential AI platform across training, fine-tuning, inference, and AI agent execution. It delivers verifiable guarantees at every stage, backed by post-quantum cryptography that protects workloads against current and future threats. For example, ServiceNow runs OPAQUE in production to extend AI capabilities to its enterprise customers without exposing their data. A national healthcare system can train diagnostic models on patient data across jurisdictions, run inference across facilities, and deploy AI agents on live clinical data – all on a single platform. The platform generates hardware-attested evidence aligned with SOC 2, ISO 27001, ISO 42001, GDPR Article 32, and EU AI Act high-risk obligations, proving data remained private, policies were enforced, and regulatory requirements were met.

SOURCE: TII

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