Alison Smith is a Director of Generative AI at Booz Allen Hamilton where she helps clients address their missions with innovative solutions. Leading Booz Allen's investments in Generative AI and grounding them in real business needs, Alison employs a pragmatic approach to designing, implementing, and deploying Generative AI that blends existing tools with additional customization. She is also responsible for disseminating best practices and key solutions throughout the firm to ensure that all teams are up-to-date on the latest available tools, solutions, and approaches to common client problems. In addition to her role at Booz Allen which balances technical solutions and business growth, Alison also enjoys staying connected to and serving her local community. From 2017-2021, Alison served on the board of a non-profit, DC Open Government Coalition (DCOGC), a group that seeks to enhance public access to government information and ensure transparent government operations; in November 2021, Alison was recognized as a Power Woman in Code by DCFemTech.

Check out all the on-demand sessions from the Intelligent Security Summit here. Plagiarism isn't limited to essays. Programming plagiarism -- where a developer copies code deliberately without attribution -- is an increasing trend. According to a New York Times article, at Brown University, more than half of the 49 allegations of academic code violations in 2016 involved cheating in computer science. At Stanford, as many as 20% of the students in a single 2015 computer science course were flagged for possible cheating, the same piece reports.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

Experts say artificial intelligence -- which has wide applications across the military, civil and private sectors -- will be critical to furthering space technology as the cosmos becomes more contested. "The space environment continues to rapidly evolve," said Melanie Stricklan, CEO of Slingshot Aerospace, a space simulation and analytics company based in Austin, Texas, and El Segundo, California. "We continue to proliferate with new users and capabilities, new sensors both on orbit looking down, and on the Earth looking back up at space." Artificial intelligence can improve space domain awareness, accelerate command-and-control decisions as well as inject resiliency into satellites and their corresponding networks, she said during an online panel discussion hosted by Booz Allen Hamilton. "There's a lot of limitations for space today, but I think AI solutions really offer a transformative opportunity for ... the protect-and-defend mission on the defense side [and] for improving operations on the commercial side," Stricklan said.

Lots of things are being called "smart" these days -- everything from light bulbs to cars. Increasingly, the smarts come from some form of artificial intelligence or machine learning. AI is no longer limited to big central data centers. By moving it to the edge, enterprises can reduce latency, improve performance, reduce bandwidth requirements, and enable devices to continue to operate even when there's no network connectivity. One of the main drivers for the use of AI at the edge is that the sheer amount of data produced in the field would cripple the internet if it all had to be processed by centralized cloud computing solutions and traditional data centers.

Lots of things are being called "smart" these days -- everything from light bulbs to cars. Increasingly, the smarts come from some form of artificial intelligence or machine learning. AI is no longer limited to big central data centers. By moving it to the edge, enterprises can reduce latency, improve performance, reduce bandwidth requirements, and enable devices to continue to operate even when there's no network connectivity. One of the main drivers for the use of AI at the edge is that the sheer amount of data produced in the field would cripple the internet if it all had to be processed by centralized cloud computing solutions and traditional data centers.

I have always worked with data, since high school, a long time ago in a galaxy far far away. Specifically, my background is astrophysics, with a Ph.D. in the subject. I performed astronomical data analysis, modeling, and simulation for 25 years, while also working on data repositories for space science satellite missions at NASA. I became very interested in the scientific discovery opportunities of very large datasets in the late 1990's, at which time I began my quest into machine learning, data mining, and data science. The motivation for me has always been discovery, from my early days until now.

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.