Tools & Platforms
SAS Chief Technology Officer named to North Carolina governor’s AI Leadership Council
CTO Bryan Harris leads the company’s software development, with a focus on trustworthy AI
CARY, N.C., Sept. 16, 2025 /PRNewswire/ — North Carolina Governor Josh Stein has named Bryan Harris, Executive Vice President and Chief Technology Officer at data and AI company SAS, to his new AI Leadership Council. Established by Gov. Stein’s executive order on artificial intelligence, the council convenes elected and appointed officials and representatives from technology and academia to advise and support the Governor and state agencies on AI strategy, policy and training. The council is dedicated to achieving the state’s goals of fostering innovation, advancing AI-driven industries and preparing the North Carolina workforce for the evolving technological landscape.
“AI has the potential to transform how we work and live, carrying with it both extraordinary opportunities and real risks,” said Governor Josh Stein. “Our state will be stronger if we are equipped to take on these challenges responsibly. I am looking forward to this council helping our state effectively deploy AI to enhance government operations, drive economic growth and improve North Carolinians’ lives.”
Harris brings over 25 years of experience in the enterprise software industry to his council role. At SAS, he is responsible for an award-winning software portfolio that empowers organizations with data and AI. He leads a global research and development (R&D) organization of nearly 4,000 people focusing on product design, engineering, advanced analytics and AI, industry solutions, modeling and quantum computing. Harris was also instrumental in the creation of the SAS Data Ethics Practice, which guides the company’s responsible innovation and trustworthy AI efforts.
“There are three key qualities that must be at the heart of trustworthy AI: speed, accuracy and consistency. When we instill these ambitions across communities, we empower people to unlock groundbreaking innovation and drive substantial economic growth,” said Harris. “I am honored to be a part of this effort in North Carolina alongside Governor Stein and accomplished civic and business leaders.”
Follow Bryan Harris and engage with him on LinkedIn.
Under Harris’ leadership, SAS launched the cloud-native SAS® Viya® platform and unified the entire portfolio across industry solutions and models. He is also known for his dedication to fostering a purpose-driven, inclusive culture within the R&D organization. He has led several initiatives to support professional growth and encourage cross-divisional collaboration, all of which contribute to SAS’ reputation as a top employer in the technology industry.
Many people worry that AI is going to take their job. But a recent survey conducted by the Federal Reserve Bank of New York found that rather than laying off workers, many AI-adopting firms are retraining their workforces to use the new technology. Yet there’s little research into whether existing job-training programs are helping workers successfully adapt to an evolving labor market.
A new working paper starts to fill that gap. A team of researchers, including doctoral candidate Karen Ni of the Harvard Kennedy School, analyzed worker outcomes after they participated in job-training programs through the U.S. government’s Workforce Innovation and Opportunity Act. Researchers looked at administrative earnings records spanning the quarters before and after workers completed training. Then they analyzed workers’ earning when transitioning from or into an occupation that was highly “AI-exposed” — a term that refers to the extent of tasks that have the potential to be automated, both in the traditional computerization sense and through generative AI technology.
Across the board, the training programs demonstrated a positive impact, with displaced workers seeing increased earnings after entering a new occupation. Still, those earnings were less for someone who targeted a high AI-exposed occupation than someone who targeted a low AI-exposed occupation.
In this edited conversation, Ni explains the role that job-training programs play as AI use is transforming the labor market.
With all the discussion around job displacement and AI, what led you to focus on retraining in particular?
When thinking about the disruptions that a new large-scale technology might have for the labor market, it’s important to understand whether it’s possible for us to help workers who might be displaced by these technologies to transition into other work. So we homed in on, OK, we know that some of these workers are being displaced. Now, what can job training services do for them? Can they improve their job prospects? Can they help them move up in terms of earnings? Is it possible to retrain some of these workers for highly AI-exposed roles?
We wanted to help document the transition and adaptability for these displaced workers, especially those who are lower income. Because then we can think about how we can support these workers, whether it be better investing in these kinds of workforce-development programs or training programs, or adapting those programs to the evolving labor market landscape.
“We wanted to help document the transition and adaptability for these displaced workers, especially those who are lower income.”
What can we learn by looking at data from government workforce development programs?
One of the big advantages of using these trainees is that it’s nationwide, and so it’s nationally representative. That allows us to take a broad look at trainees across the entire country and capture a fair bit of heterogeneity in terms of their occupations and backgrounds. For the large part, our sample captures displaced workers who tend to be lower income, making an average of $40,000 a year. Some are making big transitions from one occupation to a completely different one. We also see a fair number of people who end up going into the same types of jobs that they had before. We think those workers are likely trying to develop new skills or credentials that might be helpful to enter back into a similar occupation. Some of these people might be displaced from their occupation because of AI. But the job displacement in this sample could be for any reason, like a regional office shutting down.
Can you provide some examples of highAI-exposed careers versus low AI-exposed careers?
AI exposure refers to the extent of tasks within an occupation that could potentially be completed by a machine or a large language model. Among our sample of job trainees, some of the most common high AI-exposed occupations were customer service representatives, cashiers, office clerks. On the other end of the spectrum, the lowest AI-exposed workers tended to be manual laborers, such as movers, industrial truck drivers, or packagers.
AI retrainability by occupation
What were your main findings?
We first looked at the split before entering job training: if they were displaced from a low AI-exposed or high AI-exposed occupation. After training, we find pretty positive earnings returns across the board. However, workers who are coming from high AI-exposed jobs have, on average, 25 percent lower earnings returns after training compared to workers initially coming from low AI-exposed occupations.
Then we looked at the split after job training, if they were targeting high AI-exposed jobs or low AI-exposed jobs. If you break it down that way, we find that workers generally are better off targeting jobs that are lower AI-exposed compared to the workers who are targeting jobs that are more highly AI-exposed. Those who are targeting the high AI-exposed fields tend to face a penalty of 29 percent in terms of earnings, relative to workers who target more general skills training.
Are there any recommendations that displaced workers could take away from those findings?
I would cautiously say our findings seem to suggest that, for these AI-exposed workers going through job-training programs, going for jobs that are less AI-exposed tends to give them a better outcome. That said, the fact that we do see positive returns for all of these groups suggests that there’s probably other factors that need to be considered. For instance, what are the specific types of training that they’re receiving? What kinds of skills are they targeting? There’s an immense heterogeneity across the different job-training centers throughout the country, in terms of the quality, intensity, and even the types of occupations that they can offer services for. There’s a lot of potential for future work to consider how those factors might affect outcomes.
Also, in this case, the training program is predominantly serving displaced workers from lower parts of the income distribution. So I don’t think we can generalize across the board and say, “everyone should go do a job-training program.” We were focused on this specific population.
You also created an AI Retrainability Index to rank occupations that both prepare workers well for jobs that are more AI-exposed and also earn more than their past occupation. What did the index reveal about which occupations are most “retrainable”?
We wanted to have a way of measuring by occupation how retrainable workers are if they were to be displaced. Our index ranking shows that, depending on where they’re starting from, you might have more or less capability of being retrained for highly AI-exposed roles. The only three occupational categories that had a positive index value — meaning that we consider these to be occupations that are highly AI-retrainable — were legal, computation and mathematics, and arts, design, and media. So someone coming from a legal profession is more retrainable for high-paying, high AI-exposed roles than someone coming from, say, a customer service job.
Overall, we found that 25 to 40 percent of occupations are AI retrainable, which, to us, is surprisingly high. You might think that if someone is coming from a lower-wage job, it might be really hard to retrain them for a job that has more AI exposure. But what we found is that there may actually be a large potential for retraining.
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Check Point Software Technologies announced Monday it will acquire Lakera, a specialized artificial intelligence security platform, as entrenched cybersecurity companies continue to expand their offerings to match the generative AI boom.
The deal, expected to close in the fourth quarter of 2025, positions Check Point to offer what the company describes as an “end-to-end AI security solution.” Financial terms were not disclosed.
The acquisition reflects growing concerns about security risks as companies integrate large language models, generative AI, and autonomous agents into core business operations. These technologies introduce potential attack vectors including data exposure, model manipulation, and risks from multi-agent collaboration systems.
“AI is transforming every business process, but it also introduces new attack surfaces,” said Check Point CEO Nadav Zafrir. The company chose Lakera for its AI-native security approach and performance capabilities, he said.
Lakera, founded by former AI specialists from Google and Meta, operates out of both Zurich and San Francisco. The company’s platform provides real-time protection for AI applications, claiming detection rates above 98% with response times under 50 milliseconds and false positive rates below 0.5%.
The startup’s flagship products, Lakera Red and Lakera Guard, offer pre-deployment security assessments and runtime enforcement to protect AI models and applications. The platform supports more than 100 languages and serves Fortune 500 companies globally. The company also operates what it calls Gandalf, an adversarial AI network that has generated more than 80 million attack patterns to test AI defenses. This continuous testing approach helps the platform adapt to emerging threats.
David Haber, Lakera’s co-founder and CEO, said joining Check Point will accelerate the company’s global mission to protect AI applications with the speed and accuracy enterprises require.
Check Point already offers AI-related security through its GenAI Protect service and other AI-powered defenses for applications, cloud systems, and endpoints. The Lakera acquisition extends these capabilities to cover the full AI lifecycle, from models to data to autonomous agents.
Upon completion of the deal, Lakera will form the foundation of Check Point’s Global Center of Excellence for AI Security. The integration aims to accelerate AI security research and development across Check Point’s broader security platform.
The acquisition is another in a flurry of bigger cybersecurity companies moving to acquire AI-focused startups. Earlier this month, F5 acquired CalypsoAI, Cato Networks acquired Aim Security, and Varonis acquired SlashNext.
The deal remains subject to customary closing conditions.
What You Should Know:
– Commure, a healthcare technology company, has announced the direct embedding of its Ambient technology within MEDITECH Expanse Now, the physician’s mobile application in the MEDITECH Expanse EHR platform.
– The collaboration empowers healthcare organizations using Expanse Now to streamline clinical documentation and reduce administrative burdens, which allows clinicians to focus more fully on patient care within their familiar mobile workflows. This solution is now available to early adopters, with general availability to follow.
A New Era for Clinical Documentation
Commure’s Ambient technology is designed to deliver real-time, AI-powered clinical documentation that fits naturally into the clinician’s workflow. By intelligently capturing and structuring patient-clinician conversations, the solution saves providers an average of 90 minutes per day. This helps reduce cognitive overload and enables clinicians to stay present with their patients.
Seamless Integration and Strategic Advantages
This integration is part of Commure’s comprehensive suite of ambient documentation solutions that address workforce shortages, inefficiencies, and administrative burdens. The Ambient Suite keeps clinicians in their workflow, supports customization and quality capture, and extends across various care settings, including ambulatory environments and the emergency department. Built with Commure’s revenue cycle expertise, the Ambient Suite enhances documentation quality by leveraging both clinical and financial insights.
The embedded offering in Expanse Now complements other previously released ambient mobile and web application options that also make use of deep bidirectional integration with MEDITECH Expanse. Commure works directly with clinicians and administrators to boost margins, reduce burdens, and improve patient engagement. The company integrates with over 60 EHRs and powers millions of encounters annually.Ian Shakil, Chief Strategy Officer of Commure, stated that integrating the company’s Ambient AI technology directly within MEDITECH Expanse Now is a significant step forward in their mission to transform healthcare organizations into “the most advanced, intelligent, and human-centered systems”. The integration uses gold-standard technology to securely exchange data and accurately upload the generated notes back into discrete sections of MEDITECH Expanse.
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