SAS Viya: AI and Governance Explained

Decoding SAS Viya: Your Guide to Powerful AI and Responsible Governance

John: Welcome, everyone, to our deep dive into a significant player in the world of enterprise AI and analytics: SAS Viya. In today’s rapidly evolving technological landscape, businesses are increasingly turning to Artificial Intelligence (AI) to unlock new opportunities and gain a competitive edge. However, with great power comes great responsibility, and that’s where the crucial concept of AI governance steps in. We’re going to unravel what SAS Viya is, how it leverages AI, and why its emphasis on AI governance is more critical than ever.

Lila: Thanks, John! I’m excited to learn alongside our readers. You mentioned SAS – they’ve been around for a while, haven’t they? What’s their background before we jump into Viya specifically?

Basic Info: Understanding SAS, Viya, AI, and Governance

John: That’s a great starting point, Lila. SAS (originally Statistical Analysis System) is a powerhouse in the analytics software space, founded way back in 1976. For decades, they’ve been a trusted partner for organizations across various industries, helping them make sense of complex data and derive actionable insights. Think of them as one of the original pioneers in turning raw data into intelligence, long before “big data” and “AI” became mainstream buzzwords.

Lila: Wow, so they have a really deep history in data. So, what exactly is SAS Viya then? Is it a new version of their classic software, or something entirely different?

John: SAS Viya is their modern, cloud-native AI, analytics, and data management platform. While it builds upon SAS’s rich analytical heritage, Viya represents a significant architectural evolution. It’s designed for the demands of today’s data-driven world – think scalability, speed, openness, and the ability to handle complex AI workloads. It’s not just an update; it’s a re-imagined platform for the AI era.

Lila: “Cloud-native” – does that mean it only works in the cloud?

John: Not exclusively, though it’s optimized for cloud environments like AWS, Microsoft Azure, and Google Cloud. “Cloud-native” (applications designed to run optimally in cloud computing environments) means it’s built using technologies like containers and microservices, which make it flexible, scalable, and resilient. This architecture allows organizations to deploy Viya in public clouds, private clouds, or even hybrid environments, depending on their needs and infrastructure.

Lila: Okay, that makes sense. Now, the “AI” part in “SAS Viya, AI, AI governance.” How does AI fit into the Viya platform? Is it just one feature, or more deeply integrated?

John: AI is absolutely integral to SAS Viya. It’s not just a bolted-on feature; Viya provides a comprehensive, end-to-end environment for the entire AI and analytics lifecycle. This includes everything from data preparation and exploration to building, deploying, and managing sophisticated AI models, including machine learning (systems that learn from data), deep learning (a subset of ML using neural networks with many layers), natural language processing (understanding human language), and computer vision (interpreting images and videos).

Lila: That sounds pretty comprehensive! So, if AI is the engine, then “AI Governance” must be the steering wheel and safety system, right? It sounds important, but also a bit… heavy. What does it actually mean in this context?

John: That’s an excellent analogy, Lila. AI Governance is precisely about ensuring that AI systems are developed and used responsibly, ethically, and in compliance with regulations and organizational policies. As AI models become more powerful and make more critical decisions – from loan applications to medical diagnoses – the potential risks also increase. These risks can include biased outcomes, lack of transparency (the “black box” problem), security vulnerabilities, and privacy violations. AI governance from SAS, for example, aims to provide a framework with processes, policies, standards, and tools to manage these risks effectively throughout the AI lifecycle. It’s about building trust in AI systems, both internally within an organization and externally with customers and the public.

Lila: So, SAS isn’t just providing the tools to build AI, but also tools and frameworks to make sure it’s used well? That seems crucial, especially with all the news about AI sometimes going off the rails.

John: Exactly. Public trust has indeed become the new currency for AI innovation, as some industry reports suggest. SAS has been quite vocal about this, emphasizing a “governance-first” philosophy. They understand that for AI to be truly adopted and beneficial in the long run, especially in regulated industries, it needs to be trustworthy. SAS Viya is designed with this principle in mind, embedding governance capabilities throughout the platform.

Supply Details: Accessing and Deploying SAS Viya

John: Now that we have a foundational understanding, let’s talk about how organizations can actually get their hands on SAS Viya. As I mentioned, SAS Viya is a software platform developed and offered by SAS. It’s not a physical product you buy off a shelf, but rather a sophisticated suite of software tools and capabilities.

Lila: So, if a company wants to use SAS Viya, how do they go about it? Is it a subscription service, or a one-time purchase?

John: SAS offers flexible licensing and deployment models to cater to diverse customer needs. Typically, it involves a subscription model, the terms of which can vary based on the scale of deployment, the specific functionalities required, and the number of users. This flexibility is key for enterprises of different sizes. Importantly, SAS Viya is available on major cloud marketplaces, such as Microsoft Azure Marketplace and AWS Marketplace. This makes procurement and deployment significantly easier for companies already invested in these cloud ecosystems.

Lila: Being on cloud marketplaces sounds convenient. Does that mean a small startup could potentially use SAS Viya, or is it mainly geared towards large enterprises?

John: While SAS has traditionally served large enterprises, the move towards cloud-native architecture and marketplace availability certainly broadens its accessibility. The platform is inherently scalable. A smaller organization might start with a more focused deployment and scale up as their needs grow. SAS also offers various solutions built on Viya that can be tailored. For instance, the recent SAS Innovate conference highlighted enhancements like SAS Viya Workbench, which supports R language coding and is available on these marketplaces, catering to data scientists who prefer specific development environments.

Lila: Are there different versions or “flavors” of SAS Viya? Like a basic version and a pro version?

John: SAS Viya is a comprehensive, integrated platform, but organizations can license specific components or “offerings” based on their requirements. For example, they might focus on SAS Visual Analytics for data visualization and exploration, SAS Intelligent Decisioning for operationalizing decisions with AI, or SAS Model Manager for overseeing the AI model lifecycle. SAS also provides add-ons and specialized tools like SAS Data Maker for synthetic data generation, which recently saw enhancements from their acquisition of Hazy. So, while Viya is the core platform, it can be configured and extended to meet precise analytical and AI objectives.

Lila: So it’s modular in a way? You can pick and choose the bits you need on top of the core Viya platform?

John: Precisely. This allows for a more tailored investment and ensures that organizations are getting the capabilities most relevant to their business challenges. The core idea is a unified environment, but with the flexibility to adapt to specific functional needs across different user roles, from data engineers and data scientists to business users and IT administrators.

Technical Mechanism: Under the Hood of SAS Viya

John: Let’s delve into the technical underpinnings of SAS Viya. As we’ve touched upon, its architecture is a key differentiator. It’s built as a cloud-native platform, leveraging microservices (a software development technique structuring an application as a collection of loosely coupled services) and containerization (packaging software code with all its necessary components like libraries and dependencies so it can run consistently on any infrastructure).

Lila: You’ve mentioned “cloud-native” and “microservices” a few times. Can you break those down a bit more for readers who might not be familiar with those terms? Why are they important for an AI platform?

John: Certainly. “Cloud-native” essentially means that SAS Viya is designed from the ground up to take full advantage of cloud computing environments. This isn’t just about running on the cloud; it’s about being architected for the elasticity (ability to grow or shrink resources as needed), scalability (handling increasing workloads), and resilience (fault tolerance) that cloud platforms offer. “Microservices” contribute to this by breaking down the platform into smaller, independent, and manageable services. Each service can be developed, deployed, and scaled independently. For an AI platform, this means greater agility, faster updates, better resource utilization, and the ability to easily integrate new AI technologies as they emerge.

Lila: Okay, that makes it clearer – it’s about being flexible and robust. So, how does SAS Viya actually *do* the AI? What’s happening under the hood when a data scientist, for example, is building a model?

John: SAS Viya supports the entire AI and analytics lifecycle. This starts with robust data management capabilities, allowing users to access, prepare, and transform data from diverse sources. For model development, Viya provides a rich set of tools and techniques. This includes traditional statistical modeling, machine learning algorithms (like decision trees, regressions, support vector machines), and advanced deep learning capabilities for tasks like image recognition or natural language understanding. It supports popular open-source languages like Python and R, alongside SAS’s own powerful programming language, allowing data scientists to work in their preferred environments. For instance, SAS Viya Workbench now includes support for R coding and SAS Enterprise Guide as an optional IDE (Integrated Development Environment – a software application that provides comprehensive facilities to computer programmers for software development).

Lila: The Apify search results mentioned “SAS Viya Intelligent Decisioning” and the new “agentic AI framework.” That sounds pretty advanced! What are AI agents in this context?

John: That’s a very current and exciting development. “Agentic AI” refers to AI systems, or “agents,” that can perceive their environment, make decisions, and take actions with a certain degree of autonomy to achieve specific goals. SAS Viya’s agentic AI framework, particularly through SAS Viya Intelligent Decisioning, allows organizations to build and deploy these intelligent AI agents. What’s key here, and SAS emphasizes this, is achieving the “just right AI autonomy to human involvement ratio.” This means these agents aren’t just running wild; their autonomy can be tailored to the task’s complexity, risk, and business goals. For example, an AI agent vetting mortgage applications might automatically approve clear-cut cases but flag more complex or borderline denials for human review. The human can then query the agent to understand its reasoning before making the final call. This approach helps strike a balance between automation and crucial human oversight.

Lila: That’s fascinating – AI that can act but still be managed. And this ties back to AI governance, I presume. How is governance technically implemented within Viya to manage these AI models and agents?

John: Precisely. Governance isn’t an afterthought in Viya; it’s woven into its fabric. Technically, this manifests in several ways:

Model Lineage and Auditability: Viya tracks the entire lifecycle of a model, from the data used to train it, the transformations applied, who developed it, and when it was deployed. This creates an auditable trail, which is crucial for regulatory compliance and troubleshooting.
Bias Detection and Explainability: The platform includes tools to help identify and mitigate bias in AI models. It also offers features for model explainability (often called XAI – Explainable AI), helping users understand why a model made a particular prediction or decision. This is vital for building trust and ensuring fairness.
Version Control and Champion/Challenger Testing: Viya supports robust version control for models and allows for “champion/challenger” testing, where new models are tested against existing ones before full deployment.
Access Controls and Security: It provides granular access controls, ensuring that only authorized users can access specific data, models, or functionalities.
Centralized Model Management: SAS Model Manager, part of Viya, offers a central repository to register, validate, deploy, monitor, and retrain models, ensuring consistent governance.

Furthermore, SAS has introduced resources like the “AI Governance Map,” an assessment tool to help organizations understand their AI governance maturity across areas like oversight, compliance, operations, and culture. And they’ve announced an upcoming holistic AI governance solution designed for executives to monitor all AI systems, models, and agents. It’s this end-to-end, integrated approach to governance that SAS is championing.

Lila: So it’s like having a built-in compliance officer and quality control manager for your AI? That sounds incredibly valuable for businesses that are serious about using AI responsibly.

John: That’s a good way to put it. It’s about providing the guardrails and oversight mechanisms necessary to navigate the complexities of AI deployment at scale, ensuring that innovation doesn’t come at the cost of responsibility or trust.

Team & Community: The People Behind and Around SAS Viya

John: A platform as comprehensive as SAS Viya isn’t just about the software; it’s also about the people behind it and the community around it. SAS, as a company, is a large, global organization with thousands of employees. This includes a vast pool of talented developers, data scientists, statisticians, engineers, and domain experts who continuously work on enhancing Viya and developing new analytical solutions.

Lila: That’s a lot of brainpower! What about the users of SAS Viya? Is there a community where they can share experiences, ask questions, or learn from each other?

John: Absolutely. SAS has cultivated a strong and active user community over its many decades. There are extensive online forums, user groups (both global and local), and dedicated support channels. SAS also invests heavily in documentation, training programs, and certification paths to help users get the most out of their software. Events like the annual SAS Innovate conference (formerly SAS Global Forum) are major gatherings for the community to learn about new developments, share best practices, and network.

Lila: Given the strong emphasis on AI ethics and governance that we’ve been discussing, are there specific teams or initiatives within SAS that focus on these areas?

John: Yes, and this is a crucial point. SAS has a dedicated Data Ethics Practice, which is actively involved in shaping the company’s approach to responsible AI. Reggie Townsend, VP of the Data Ethics Practice at SAS, often speaks about the importance of assessing intended use and expected outcomes before AI deployment and monitoring for ongoing compliance. This isn’t just a PR exercise; it’s about embedding ethical considerations into product development and company culture. SAS also offers “AI Governance Advisory” services, as mentioned in the Apify results, where they consult with organizations like PZU (an insurance company) to create custom best practices for AI governance, covering oversight, innovation goals, and more. This demonstrates a commitment that goes beyond just selling software.

Lila: So, it’s not just talk; they’re actively helping companies implement responsible AI. That’s reassuring. It feels like they’re trying to build an ecosystem of trust around their AI offerings.

John: That’s the goal. Building trust in AI requires a multi-faceted approach: robust technology, strong ethical principles, a supportive community, and proactive guidance. SAS appears to be working on all these fronts. The community aspect also plays a role in sharing knowledge about ethical challenges and solutions, making it a collective effort.

Use-Cases & Future Outlook: Where SAS Viya Shines and Where It’s Headed

John: SAS Viya’s capabilities find application across a vast spectrum of industries due to its versatility in handling data and building AI models. In finance, it’s used for fraud detection, credit risk scoring, anti-money laundering, and regulatory compliance. In healthcare, applications include predicting patient outcomes, optimizing hospital operations, clinical trial analysis, and as we saw in the recent announcements, models for Medication Adherence Risk. Retailers use it for demand forecasting, customer segmentation, personalized marketing, and supply chain optimization. Manufacturing benefits from predictive maintenance, quality control, and optimizing production processes – the new Strategic Supply Chain Optimization model is an example here. And in the public sector, it’s applied to areas like tax compliance (e.g., new models for Sales Tax and Individual Income Tax), benefits program integrity (like the Payment Integrity for Food Assistance model), and public safety.

Lila: You mentioned those prebuilt models again. The Apify results highlighted that SAS introduced six custom AI models, with more on the way. How do these help companies, especially those without big data science teams?

John: This is a very strategic move by SAS. As Udo Sglavo, VP of Applied AI and Modeling at SAS, pointed out, these models address two market segments. For companies *with* data science teams, these prebuilt models can handle common, well-defined problems, freeing up their data scientists to focus on more complex, unique strategic challenges. For companies *without* extensive data science resources, these models offer a quick way to get started with AI and see tangible business impact almost immediately. Models like “AI-driven Entity Resolution” or “Document Analysis” have broad applicability, while others are very industry-specific. They are designed to be lightweight, easy to deploy (often in containers), and built around real-world use cases, delivering value quickly.

Lila: That sounds like a smart way to democratize AI a bit. So, looking ahead, what’s the future outlook for SAS Viya and its AI capabilities? Are we going to see more AI agents, more generative AI features like the Viya Copilot?

John: Absolutely. The future for SAS Viya is firmly rooted in advancing practical, trusted AI. We can expect continued development in several key areas:

Generative AI: The SAS Viya Copilot, built on Microsoft Azure AI Services and currently in private preview, is a clear indication of this direction. It aims to be a personal assistant for developers, data scientists, and business users within the Viya platform. We’ll likely see its capabilities expand beyond the initial offering in Model Studio for AI-powered model development and code assistance.
Agentic AI: The new agentic AI framework within Viya is just the beginning. Expect more sophisticated capabilities for designing AI agents with varying degrees of autonomy, always with that crucial built-in governance and human oversight.
Synthetic Data Generation: SAS Data Maker, enhanced by the Hazy acquisition, will become even more critical for addressing data privacy and scarcity. The ability to produce multi-table and time-series synthetic data is a significant step.
Deeper Governance Integration: As AI becomes more pervasive, the need for robust, transparent, and auditable governance will only grow. SAS will likely continue to enhance Viya’s built-in governance features and expand its advisory offerings. The upcoming “unified holistic AI governance solution” for executives points to this.
More Industry-Specific Solutions: The rollout of prebuilt models will continue, catering to specific industry needs and helping organizations accelerate AI adoption.
Cloud and Openness: Continued strengthening of its cloud-native capabilities and integrations with open-source ecosystems will be key to maintaining relevance and flexibility.

The overarching theme, as highlighted by ITPro’s article, is that “public trust has become the new currency for AI innovation.” SAS is positioning Viya to be a platform that enables innovation while fostering that trust through responsible AI practices.

Lila: It sounds like they’re trying to make AI less of a scary black box and more of a reliable, understandable tool for businesses.

John: Precisely. The goal is to move AI from experimental Hype to everyday Help, responsibly and effectively.

Competitor Comparison: SAS Viya in the AI Platform Landscape

John: The AI and analytics platform market is, as you can imagine, quite competitive. SAS Viya operates in a space with other major players. For instance, you have comprehensive cloud-based AI platforms from the hyperscalers: Google Cloud AI Platform (Vertex AI), Amazon SageMaker (part of AWS), and Microsoft Azure AI. Then there are specialized analytics and data science platforms like Databricks, known for its Lakehouse architecture, and open-source driven solutions.

Lila: That’s a crowded field! So, how does SAS Viya differentiate itself? What are its unique selling points when a company is choosing an AI platform?

John: SAS Viya has several key differentiators:

End-to-End Lifecycle Management: While many platforms excel in specific areas, SAS Viya offers a truly integrated environment that covers the entire analytics lifecycle – from data preparation and management, through model development and deployment, to decisioning and ongoing governance. This holistic approach can simplify workflows and reduce the need to stitch together multiple disparate tools.
Unparalleled Governance and Trustworthiness: This is a cornerstone of SAS’s philosophy and a core strength of Viya. The platform has governance built-in, not bolted on. Features for model explainability, bias detection, auditability, and lineage are deeply integrated. Their emphasis on “responsible innovation” and resources like the AI Governance Map and advisory services further underscore this commitment. As InfoWorld and other sources note, this “governance-first philosophy” is crucial for enterprise use, especially in regulated industries.
Decades of Industry Expertise: SAS has nearly 50 years of experience working with complex data and analytics challenges across virtually every industry. This deep domain knowledge is embedded in their software and informs the development of industry-specific solutions and prebuilt models, helping organizations achieve faster time-to-value.
Openness and Integration with Flexibility: While SAS has its own powerful programming language and tools, Viya is increasingly open. It supports Python, R, and other open-source tools, allowing data scientists to leverage their existing skills and preferred environments. It can also integrate with various data sources and existing enterprise systems. SAS Viya Workbench, for example, now offering SAS Enterprise Guide as an optional IDE, shows this blend of SAS’s robust environment with flexible coding options.
Proven Reliability and Scalability: SAS has a long-standing reputation for producing robust, reliable software that can handle complex, mission-critical analytical workloads at scale. Viya continues this tradition with its modern, cloud-native architecture.
Focus on Intelligent Decisioning: SAS Viya Intelligent Decisioning, coupled with the new agentic AI framework, allows organizations to not just build models but to operationalize AI-driven decisions directly into business processes with tailored human oversight. This is about making AI actionable.

Lila: So, it’s not just about having the flashiest new AI algorithm, but about making AI work reliably, responsibly, and effectively within a business context to solve real problems?

John: Exactly, Lila. It’s about operationalizing AI with confidence and ensuring that the insights generated lead to trusted decisions. While other platforms might offer cutting-edge individual components, SAS Viya’s strength lies in its cohesive, governed, and enterprise-ready approach to the entire AI and analytics journey.

Risks & Cautions: Navigating the Challenges of AI Implementation

John: While platforms like SAS Viya offer immense potential, it’s important to acknowledge the general risks associated with AI and some considerations specific to adopting any large-scale enterprise platform.

Lila: Let’s start with the general AI risks. We hear a lot about bias and “black box” models. What should organizations be mindful of?

John: The common AI risks are significant and require constant attention:

Bias in Data and Models: AI models learn from data. If that data reflects historical biases (e.g., societal biases in hiring or lending), the AI model will likely perpetuate and even amplify those biases. This can lead to unfair or discriminatory outcomes.
Lack of Transparency (The “Black Box” Problem): Some complex AI models, particularly deep learning models, can be difficult to interpret. Understanding *why* a model made a specific decision is crucial for trust, debugging, and regulatory compliance.
Potential for Misuse: Powerful AI technologies can be misused if they fall into the wrong hands or are deployed without ethical considerations (e.g., in autonomous weapons or pervasive surveillance).
Data Privacy Concerns: AI systems often require large amounts of data, raising concerns about how that data is collected, stored, used, and protected, especially with regulations like GDPR or CCPA. SAS Data Maker, which generates synthetic data, is one way SAS is helping to address this.
Security Vulnerabilities: AI models can be susceptible to specific types of attacks, such as adversarial attacks (subtly manipulated inputs that cause the model to make incorrect predictions) or data poisoning.
Job Displacement Fears: While AI is expected to create new jobs, there are legitimate concerns about its impact on existing roles as automation capabilities increase.

Lila: Those are some serious considerations. What about risks or cautions specific to implementing a platform like SAS Viya? Even with all its governance features, it can’t be completely plug-and-play, can it?

John: No, implementing any comprehensive enterprise platform comes with its own set of challenges:

Complexity and Learning Curve: While SAS strives for user-friendliness with tools like low-code/no-code interfaces (as seen in SAS Viya Intelligent Decisioning) and the new SAS Viya Copilot, a platform with such extensive capabilities will inevitably have a learning curve for users and administrators. Proper training and skill development are essential.
Cost of Investment: Enterprise-grade software like SAS Viya represents a significant investment, not just in licensing fees but also in terms of the resources needed for implementation, customization, and ongoing maintenance. Organizations need to carefully evaluate the total cost of ownership (TCO) and potential return on investment (ROI).
Integration with Existing Systems: Integrating Viya into an organization’s existing IT landscape, data sources, and workflows can be complex and requires careful planning and execution.
Vendor Lock-in Concerns: While SAS is promoting openness, relying heavily on a single vendor’s platform can sometimes lead to concerns about vendor lock-in. However, Viya’s support for open-source languages and its availability on multiple cloud platforms mitigate this to some extent.
Over-reliance on AI and Automation: Even with sophisticated AI, human oversight and critical thinking remain indispensable. There’s a risk of organizations becoming overly reliant on automated decisions without adequate human validation, especially in sensitive areas. SAS’s “just right AI autonomy to human involvement ratio” concept for AI agents directly addresses this.
Change Management: Successfully adopting an AI platform often requires significant changes in business processes, organizational culture, and employee skill sets. Effective change management is crucial for realizing the full benefits.

Lila: So, even with strong built-in governance tools like those in SAS Viya, the ultimate responsibility still lies with the organization using the AI?

John: Absolutely. AI governance is a socio-technical challenge. Technology like SAS Viya provides essential tools and frameworks, but these must be complemented by robust internal policies, ethical guidelines, ongoing training, a culture of responsibility, and active human oversight. As Reggie Townsend from SAS noted, it’s about oversight, operations, *and* organizational culture. SAS’s AI Governance Advisory offerings acknowledge this by helping organizations establish these broader practices.

Expert Opinions / Analyses: What Industry Watchers Say

John: It’s always insightful to see what independent analysts and industry experts are saying. The recent SAS Innovate conference and its announcements have garnered attention.

For example, Robert Kramer, VP & Principal Analyst at Moor Insights & Strategy, commented on the SAS Viya platform updates. He noted, “While these updates may not be groundbreaking, they integrate features with built-in governance and ready-to-use models, crucial for enterprise use.” He added that customers could benefit from “faster onboarding, easier collaboration, and more secure AI development, especially in regulated industries where auditability and model transparency matter.” This aligns with what we’ve discussed about SAS’s focus on practical, governed AI.

Lila: So, the practicality and the built-in governance are seen as key strengths for businesses? That makes sense for companies that need reliable solutions rather than just experimental tech.

John: Precisely. He also commented on the prebuilt AI models, saying, “The availability of pre-built AI models for applications such as fraud detection, supply chain planning, and health risk assessment should help organizations accelerate their AI adoption by providing ready-to-use solutions.” This speaks to the value SAS is trying to deliver quickly to its customers.

Then there’s Abhishek Punjani, Research Analyst – AI at Info-Tech Research Group. He had some very pointed observations. He stated, “In the race to innovate with AI, many organizations made a fundamental misstep early on in their AI journey by putting innovation and speed above control, sometimes at the cost of long-term resilience.” He sees SAS as helping to correct this, noting, “With its latest and agentic AI innovations, SAS is at the forefront of the industry’s movement toward a more balanced and responsible path forward. Through a combination of ethical calibrations, SAS intends to create enterprise value through AI systems that are impactful and ethically sound, allowing for tailored levels of human oversight and intervention.”

Lila: “A more balanced and responsible path forward” – that’s a strong endorsement of their approach! It sounds like analysts appreciate that SAS isn’t just chasing hype.

John: Indeed. Punjani further elaborated on the practical tools, saying, “Building on this governance-first philosophy, SAS has also expanded its Viya platform with a suite of tools aimed at practical AI enablement. SAS Data Maker addresses one of the most prominent issues in AI today, data scarcity and privacy, by generating secure synthetic data for safe model training. SAS Viya Intelligent Decisioning enables organizations to build AI agents with customized human involvement, allowing users to embed policy, logic, and rules into their AI agents for adaptive actions.”

His concluding thought was quite powerful: “Together, these solutions mark a shift toward more grounded, enterprise-ready AI. Rather than chasing scale alone, they reflect a growing focus on control and accountability, qualities that are becoming essential as AI becomes central to important business operations… It’s a reminder that the next phase of AI adoption won’t be driven by scale alone, but by how well these systems can integrate into business processes with clarity.”

Lila: “Control and accountability” – those words keep coming up. It really seems to be SAS’s core message with Viya and AI governance.

John: They are, and it resonates with the needs of many organizations, especially larger enterprises and those in regulated sectors. The “AI Governance Map” and the upcoming unified AI governance solution mentioned in the news also signal SAS’s continued investment in this critical area. An ITPro article specifically highlighted that “Public trust has become the new currency for AI innovation,” and that leaders should make AI governance their primary focus, reinforcing why SAS is “ringing the alarm bell on AI governance for enterprises.”

Latest News & Roadmap: What’s New and What’s Next for SAS Viya

John: SAS is continuously evolving its Viya platform, and their recent SAS Innovate conference was packed with announcements that give us a clear view of their current focus and future direction. We’ve touched on some of these, but let’s consolidate the key highlights.

For the SAS Viya platform itself:

SAS Data Maker: This synthetic data generator has been significantly enhanced, incorporating technology from SAS’s recent acquisition of Hazy. It now offers improved capabilities for creating multi-table and time-series synthetic data, crucial for tackling data privacy and scarcity. It’s moving from private to public preview and expected to be generally available in Q3 of this year.
SAS Viya Intelligent Decisioning: Now generally available, this tool helps users build and deploy intelligent AI agents with a low-code/no-code interface. The emphasis is on achieving that “just right AI autonomy to human involvement ratio” for optimal oversight.
SAS Viya Copilot: Currently in private preview, this AI-driven conversational assistant is embedded directly into the Viya platform. Built on Microsoft Azure AI Services, it aims to assist developers, data scientists, and business users. The initial offering focuses on AI-powered model development and code assistance in Model Studio, with general availability expected in Q3.
SAS Viya Workbench: Released in 2024, this development environment now supports R language coding, offers SAS Enterprise Guide as an optional IDE, and is available on both Microsoft Azure Marketplace and AWS Marketplace.

Lila: Those are some significant updates! The Copilot sounds particularly interesting for making complex tasks easier. What about those prebuilt models you mentioned earlier?

John: Yes, that’s another major area of development. SAS introduced six new custom AI models designed to address specific industry processes:

Two cross-industry models: AI-driven Entity Resolution and Document Analysis.
For healthcare: Medication Adherence Risk.
For manufacturing: Strategic Supply Chain Optimization.
For the public sector: Payment Integrity for Food Assistance and Tax Compliance for Sales Tax.

And they’ve already announced four more models coming later this year: Fraud Decisioning for Payments and Card Models (banking), Payment Integrity for HealthCare (healthcare), Worker Safety Monitoring (manufacturing), and Tax Compliance for Individual Income Tax (public sector). These are designed to be lightweight, containerized, and easily deployable to deliver rapid value.

Lila: And on the governance front, which we’ve emphasized so much?

John: SAS is doubling down there as well. They’ve introduced the AI Governance Map, an assessment tool to help organizations evaluate their AI governance maturity across oversight, compliance, operations, and culture. This tool provides tailored reports and expert guidance. Furthermore, SAS announced an upcoming product, described as “a unified holistic AI governance solution” for executives, designed to aggregate, orchestrate, and monitor AI systems, models, and agents. This shows a clear roadmap towards more comprehensive and accessible AI governance tools.

Lila: It sounds like a very active development pipeline. So, looking broadly at their roadmap, what are the overarching themes we can expect SAS to pursue with Viya?

John: The general trajectory for SAS Viya seems to be:

Advancing Practical Generative AI: Expanding copilot functionalities and responsibly integrating more large language model (LLM) capabilities to enhance user productivity and insight generation.
Sophisticated and Governed Agentic AI: Further developing the framework for AI agents that can act with appropriate autonomy, always under a strong governance umbrella.
Strengthening Data Fabric and Synthetic Data Capabilities: Making it easier to access, manage, and utilize diverse data sources securely and ethically, with synthetic data playing a key role in privacy and model training.
Deepening Cloud-Native Integration and Openness: Continuing to optimize Viya for cloud environments and ensuring seamless interoperability with open-source tools and the broader data ecosystem.
Championing Trustworthy AI: Persistently enhancing features related to fairness, explainability, transparency, and auditability in AI models and processes.
Expanding Industry-Specific Solutions: Delivering more pre-built models, tailored applications, and domain-specific expertise to help organizations solve their most pressing challenges.

The consistent message is enabling powerful AI while ensuring it’s responsible, ethical, and trustworthy. As SiliconAngle and other outlets have reported, SAS is focused on “building trusted AI with SAS Viya and cloud innovation.”

FAQ: Quick Answers to Common Questions

Lila: This has been incredibly detailed, John! Let’s wrap up with a quick FAQ section for readers who might want some key takeaways.

John: Excellent idea, Lila. Fire away.

Lila: Okay, first up: What is SAS Viya in simple terms?

John: In simple terms, SAS Viya is an advanced software platform that helps businesses and organizations use their data and Artificial Intelligence (AI) to make smarter, faster decisions. Crucially, it’s designed to do this in a way that is responsible, ethical, and well-managed through strong AI governance features.

Lila: Next question: Who is SAS Viya for?

John: SAS Viya is for a wide range of users within an organization. This includes:

Data Scientists and AI Developers: Who use it to build, train, and deploy sophisticated AI and machine learning models.
Business Analysts: Who use it to explore data, create visualizations, and gain insights without needing to be expert coders.
IT Professionals: Who manage and maintain the platform, ensuring it runs smoothly and securely.
Business Leaders and Executives: Who rely on the insights and decisions generated by the platform and need assurance that AI is being used responsibly and effectively.

Lila: Makes sense, it’s quite versatile. Now, a really important one: Why is AI governance so important with SAS Viya?

John: AI governance is critical because as AI becomes more powerful and influential in business decisions (like approving loans, guiding medical treatments, or optimizing supply chains), the potential risks also increase. These include biased outcomes, lack of transparency, and misuse. SAS Viya emphasizes AI governance to help organizations:

Ensure AI is used ethically and fairly.
Comply with laws and regulations.
Build trust with customers and stakeholders.
Manage and mitigate risks associated with AI.
Maintain control and oversight over AI systems.

SAS embeds governance features throughout Viya to support this, making it a core part of their AI philosophy.

Lila: Good to reiterate that! Here’s one for the tech-savvy folks: Can SAS Viya work with open-source tools like Python or R?

John: Yes, absolutely. SAS Viya is designed to be an open platform. While it has its own powerful SAS programming language, it also fully supports and integrates with popular open-source languages like Python and R. This allows data science teams to use the tools and languages they are most comfortable with, or that are best suited for a particular task, all within the governed and scalable Viya environment. The SAS Viya Workbench, for example, explicitly supports Python and R coding.

Lila: And lastly, a practical concern: How does SAS Viya help with data privacy?

John: SAS Viya helps with data privacy in several ways:

SAS Data Maker: This tool allows organizations to generate high-quality synthetic data. Synthetic data mimics the statistical properties of real data but doesn’t contain actual sensitive information, making it safe for model training and testing while protecting individual privacy.
Robust Security and Access Controls: Viya has built-in security features and granular access controls, ensuring that only authorized personnel can access specific datasets or sensitive information.
Governance Framework: The overall AI governance framework supported by Viya encourages practices that align with data privacy regulations, such as data minimization and purpose limitation.
Anonymization and Pseudonymization Techniques: While not exclusive to Viya, the platform can be used to implement these techniques to further protect data.

By providing these capabilities, SAS Viya helps organizations leverage their data for AI while adhering to increasingly stringent data privacy requirements.

SAS Viya: Unlocking Powerful AI with Responsible Governance

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