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Mastercard has expanded its US...

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Undergraduate college enrollment fell again this spring, down nearly 5% from a year ago. That means 727,000 fewer students, according to new data from the National Student Clearinghouse.

"That's really dramatic," says Doug Shapiro, who leads the clearinghouse's research center. Fall enrollment numbers had indicated things were bad, with a 3.6% undergraduate decline compared with a year earlier, but experts were waiting to see if those students who held off in the fall would enroll in the spring. That didn't appear to happen.

"Despite all kinds of hopes and expectations that things would get better, they've only gotten worse in the spring," Shapiro says. "It's really the end of a truly frightening year for higher education. There will be no easy fixes or quick bounce backs."

Overall enrollment in undergraduate and graduate programs has been trending downward since around 2012, and that was true again this spring, which saw a 3.5% decline — seven times worse than the drop from spring 2019 to spring 2020.

The National Student Clearinghouse attributed that decline entirely to undergraduates across all sectors, including for-profit colleges. Community colleges, which often enroll more low-income students and students of color, remained hardest hit by far, making up more than 65% of the total undergraduate enrollment losses this spring. On average, U.S. community colleges saw an enrollment drop of 9.5%, which translates to 476,000 fewer students.

"The enrollment landscape has completely shifted and changed, as though an earthquake has hit the ground," says Heidi Aldes, dean of enrollment management at Minneapolis Community and Technical College, a community college in Minnesota. She says her college's fall 2020 enrollment was down about 8% from the previous year, and spring 2021 enrollment was down about 11%.

"Less students are getting an education"

Based on her conversations with students, Aldes attributes the enrollment decline to a number of factors, including being online, the "pandemic paralysis" community members felt when COVID-19 first hit, and the financial situations families found themselves in.

"Many folks felt like they couldn't afford to not work and so couldn't afford to go to school and lose that full-time income," Aldes says. "There was so much uncertainty and unpredictability."

A disproportionately high number of students of color withdrew or decided to delay their educational goals, she says, adding to equity gaps that already exist in the Minneapolis area.

"Sure, there is a fiscal impact to the college, but that isn't where my brain goes," Aldes says. "There's a decline, which means there are less students getting an education. That is the tragedy, that less students are getting an education, because we know how important education is to a successful future."

To help increase enrollment, her team is reaching out to the high school classes of 2020 and 2021, and they're contacting students who previously applied or previously enrolled and stopped attending. She says she's hopeful the college's in-person offerings — which now make up nearly 45% of its classes — will entice students to come back, and appeal to those who aren't interested in online courses. So far, enrollment numbers for fall 2021 are up by 1%. "We are climbing back," she says.

A widening divide

Despite overall enrollment declines nationally, graduate program enrollments were up by more than 120,000 students this spring. That means there are more students who already have college degrees earning more credentials, while, at the other end of the spectrum, students at the beginning of their higher ed careers are opting out — a grim picture of a widening gap in America.

"It's kind of the educational equivalent of the rich getting richer," Shapiro says. "Those gaps in education and skills will be baked into our economy, and those families' lives, for years to come."

The value of a college degree — and its impact on earning power and recession resilience — has only been reinforced by the pandemic. According to the U.S. Bureau of Labor Statistics, Americans with a college degree were more likely to stay employed during the pandemic, and if they did lose a job, they were more likely to get hired again. Unemployment rates were higher for those without a degree or credential beyond high school.

"Almost all of the income gains and the employment gains for the last decade have gone to people with higher education degrees and credentials," Shapiro says. "Those who are getting squeezed out of college today, especially at community colleges, are just getting further and further away from being able to enjoy some of those benefits."

In the National Student Clearinghouse data, traditional college-aged students, those 18 to 24, were the largest age group missing from undergraduate programs. That includes many students from the high school class of 2020, who graduated at the beginning of the pandemic. Additional research from the Clearinghouse shows a 6.8% decline in college-going rates among the class of 2020 compared to the class of 2019 — that's more than four times the decline between the classes of 2018 and 2019. College-going rates were worse for students at high-poverty high schools, which saw declines of more than 11%.

For the communities and organizations tasked with helping high school graduates transition and succeed in college, the job this year is exponentially harder. Students have always struggled to attend college: "It's not new to us," says Nazy Zargarpour, who leads the Pomona Regional Learning Collaborative, which helps Southern California high school students enroll and graduate from college. "But this year, it's on steroids because of COVID."

Her organization is offering one-on-one outreach to students to help them enroll or re-enroll in college. As part of that effort, Zargarpour and her colleagues conducted research to help them understand why students didn't go on to college during the pandemic.

"Students told us that it's a variety of things, including a lot of just life challenges," she says. "Families being disrupted because of lack of work; families being disrupted because of the challenges of the illness itself; students having to take care of their young siblings; challenges with technology."

The biggest question now: Will those students return to college? Experts say the farther a student gets from their high school graduation, the less likely they are to enroll, because life gets in the way. But Zargarpour is hopeful.

"It will take a little bit of time for us to catch up to normal and better, but my heart can't bear to say all hope is lost for any student ever."

This content is from Southern California Public Radio. View the original story at SCPR.org.

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In an era of rapidly evolving Artificial Intelligence/Large Language Model (AI/LLM) technologies, cybersecurity practitioners face significant challenges in monitoring unauthorized AI solutions, detecting AI vulnerabilities, and identifying unexpected AI/LLM development. Tenable Security Center leverages advanced detection technologies - agents, passive network monitoring, dynamic application security testing, and distributed scan engines - to surface AI/LLM software, libraries, and browser plugins. The risk managers utilize this report to begin a comprehensive review of the AI/LLM packages in systems and web applications, along with associated vulnerabilities, mitigating risks of exploitation, data leakage, and unauthorized resource consumption.

AI/LLM technologies are promising and can transform many industries and businesses, offering new innovation and efficiency opportunities. However, the technology represents a huge security challenge at many layers and this impact should not be overlooked. By using Tenable Security Center and Tenable Web App Scanning the organization is able to take a security-first approach. When combined with best practices and robust governance policies, the organizations can harness the power of AI/LLM and mitigate the associated emerging threats.  

This report provides a detailed view of AI/LLM use by leveraging Nessus plugin (196906) to detect AI/LLM usage in 3 ways: browser extensions, applications, and file paths all common to AI/LLM implementations. AI/LLM vulnerabilities discovered in web applications are identified using the Web Application Scanner, alongside network traffic analysis using the Nessus Network Monitor (NNM). By combining all methods of data collection, the risk managers are able to identify problem areas and other risks associated with AI/LLM.

Chapters

Executive Summary: This chapter provides a detailed view of AI/LLM use by leveraging Nessus plugin (196906) to detect AI/LLM usage in 3 ways: browser extensions, applications, and file paths all common to AI/LLM implementations. AI/LLM vulnerabilities discovered in web applications are identified using the Web Application Scanner, alongside network traffic analysis using the Nessus Network Monitor (NNM). 

AI/LLM Browser Extensions Chapter: This chapter provides the details for Assets that we found to have one or more of the AI/LLM detection browser extension plugins detected by Nessus. The chapter will search through plugins that include certain keywords: GPT, CopIlot, or AI. The AI/LLM Browser Detection chart shows the count of plugins related to the detection of GPT, AI, or Copilot AI or LLM browser extensions. 

AI/LLM Software Detected Using Nessus: This chapter provides the details for Assets that we found to have one or more of the AI/LLM software plugins detected by Nessus. The introductory matrix provides an indicator for all the matches found in the organization, followed by a a chart and table showing the affected assets.

AI/LLM Usage Detected Using Web Application Security: This chapter provides the details for web application assets that we found to have one or more of the AI/LLM detection plugins. The LLM/AI Usage Detected by WAS chart shows the count of plugins related to the detection of AI or LLM. 

In an era of rapidly evolving Artificial Intelligence/Large Language Model (AI/LLM) technologies, cybersecurity practitioners face significant challenges in monitoring unauthorized AI solutions, detecting AI vulnerabilities, and identifying unexpected AI/LLM development. Tenable Security Center leverages advanced detection technologies - agents, passive network monitoring, dynamic application security testing, and distributed scan engines - to surface AI/LLM software, libraries, and browser plugins. The risk managers utilize this dashboard to begin a comprehensive review of the AI/LLM packages in systems and web applications, along with associated vulnerabilities, mitigating risks of exploitation, data leakage, and unauthorized resource consumption.

This dashboard provides a detailed view of AI/LLM use by leveraging Nessus plugin (196906) to detect AI/LLM usage in 3 ways: browser extensions, applications, and file paths all common to AI/LLM implementations. AI/LLM vulnerabilities discovered in web applications are identified using the Web Application Scanner, alongside network traffic analysis using the Nessus Network Monitor (NNM). By combining all methods of data collection, the risk managers are able to identify problem areas and other risks associated with AI/LLM.

AI/LLM technologies are promising and can transform many industries and businesses, offering new innovation and efficiency opportunities. However, the technology represents a huge security challenge at many layers and this impact should not be overlooked. By using Tenable Security Center and Tenable Web App Scanning the organization is able to take a security-first approach. When combined with best practices and robust governance policies, the organizations can harness the power of AI/LLM and mitigate the associated emerging threats.

Components

AI/LLM Software Known to Nessus: This matrix provides summary counts of AI/LLM use by leveraging Nessus plugin (196906). This column chart uses the Plugin ID filter and Plugin Output filter to show the AI/LLM applications found on assets scanned using Nessus. Each label displayed is the pattern added to the plugin output search. This search allows the security managers a simplified approach to beginning the understanding into AI usage.

AI/LLM Top 5 Applications: This matrix provides the counts top 5 utilized AI/LLM applications discovered by Tenable Research. The cells use the Plugin ID (196906) and Plugin Output filter to show the AI/LLM application. Each label displayed is the pattern added to the plugin output search. This search allows the security managers a simplified approach to beginning the understanding into AI usage. The top 5 utilized list consists of: TensorFlow, NumPy, H2O Flow, HuggingFace, and scikit-learn.

AI/LLM Detection Statistics: This matrix provides summary counts of AI/LLM use by leveraging Nessus plugin (196906) to detect AI/LLM usage in 3 ways: browser extensions, applications, and file paths all common to AI/LLM implementations. AI/LLM vulnerabilities discovered in web applications are identified using the Web App Scanner, alongside network traffic analysis using the Nessus Network Monitor (NNM). By combining all methods of data collection, the risk managers are able to identify problem areas and other risks associated with AI/LLM.

AI/LLM Software Detected by NNM & WAS: AI/LLM vulnerabilities discovered in web applications are identified using the Web Application Scanner, alongside network traffic analysis using the Nessus Network Monitor (NNM). By combining all methods of data collection, the risk managers are able to identify problem areas and other risks associated with AI/LLM.

AI/LLM Browser Extensions: This matrix provides summary counts of AI/LLM use by leveraging Nessus plugin (196906). The cells use the Plugin ID filter and Plugin Output filter to show the AI/LLM browser extensions grouped into three search patterns. Each label displayed is the pattern added to the plugin output search. This search allows the security managers a simplified approach to beginning the understanding into AI usage.
 

From prehistoric cave art to today's social media feeds, to design is to be human. This hour, designer Debbie Millman guides us through a world made and remade—and helps us design our own paths.

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From prehistoric cave art to today's social media feeds, to design is to be human. This hour, designer Debbie Millman guides us through a world made and remade—and helps us design our own paths.

Learn more about sponsor message choices: podcastchoices.com/adchoices

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Comments

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