Deepfakes –mostly falsified videos and images combining the terms “deep learning” and “fake” – weren’t limited in 2019 to the Nixon presentation and were not uncommon before that. But today they are more numerous and realistic-looking and, most important, increasingly dangerous. And there is no better example of that than the warning this month (March 2021) by the FBI that nation-states are virtually certain to use deepfakes to help propagate increasingly misleading campaigns in the U.S. in coming weeks.
It’s undeniable that Machine Learning (ML) is changing the game for securing cloud infrastructure. Security vendors have rapidly adopted ML as part of their solutions, and for good reason: By analyzing massive quantities of data, it can help identify threats, speed incident response, and ease the burden on over-taxed security operations teams.
Small-to-medium-sized businesses (SMBs) have a number of unique considerations when it comes to video surveillance. For starters, with SMBs, managing security and risk often falls to a manager, store owner, or hourly security professional. Therefore, the convenience of being able to view multiple sites at once whether remotely or onsite is paramount.
NOAA’s Satellite and Information Service (NESDIS) has signed an agreement with Google to explore the benefits of Artificial Intelligence (AI) and Machine Learning (ML) for enhancing NOAA’s use of satellite and environmental data.
A couple of months ago, I described in this column how security professionals could unify a divided country. I chose a mask as a symbol of that cohesiveness. But that thin piece of fabric worn around the mouth and nose can also be a gag — a barrier that distances leaders and stifles communication.
Security operations centers (SOCs) across the globe are most concerned with advanced threat detection and are increasingly looking to artificial intelligence (AI) and machine learning (ML) technologies to proactively safeguard the enterprise, according to a new study by Micro Focus, in partnership with CyberEdge Group.
Financial services institutions and banks around the globe face monumental challenges as they look to streamline service delivery for customer transactions, manage multi-party loan processes, collaborate on industry benchmarks and indices, and eliminate fraud and cybercrime. Historically the market has primarily relied upon manual approaches for sharing and managing transaction data. But advances in confidential computing (sometimes called CC or trusted computing), combined with federated machine learning (FML), are helping financial organizations better share data and outcomes, while alleviating many privacy and security concerns.
With a growing need to improve the security, efficiency and accuracy of passenger and baggage screening, the Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program is working with a small business to advance explosive detection equipment. Synthetik Applied Technologies was awarded funding to develop machine learning training data that simulates human travelers and baggage object models to support machine learning algorithms.