DoD Simplifies Process for Defense Contractors to Comply With Cybersecurity Rules
The Defense Department released for public inspection the final cybersecurity maturity model certification program rule. The rule includes changes which…
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The Defense Department released for public inspection the final cybersecurity maturity model certification program rule. The rule includes changes which…
The relentless process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the point of failure in an otherwise formidable defense. Given that few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely to be exploited and focus efforts toward remediating those vulnerabilities first.
Artificial intelligence (AI) fuels not only the technological advancements in our businesses and homes but also redefines the operational frameworks of governments, military, and the broader societal constructs. The essence of this transformative power, however, finds its most compelling narrative in leadership and organizational health, where machine learning (ML), a subset of AI, plays a pivotal role. AI/ML can be used to support leaders in their efforts to manage and monitor initiatives and drive decisions. ML algorithms using continuous data collection activities can assist and support organization’s leaders through understanding if implemented initiatives are impacting operations by improving staff cross-collaboration and productivity while improving product and service innovation. Using predictive analytics to analyze trending data to predict future outcomes, leaders can determine if staying the course or pivoting in a certain direction is needed.
As data becomes more commoditized across all echelons of the U.S. Department of Defense, developing artificial intelligence/machine-learning (AI/ML) solutions allows for advanced data analysis and processing. However, these solutions require intimate knowledge of the relevant data as well as robust test and evaluation (T&E) procedures to ensure performance and trustworthiness. This article presents a case study and recommendations for developing and evaluating small-scale AI solutions. The model automates an acoustic event location system.
Military commanders have used information throughout warfare to influence, mislead, disrupt, or otherwise affect the enemy’s decision-making and capabilities. This article discusses the history of information operations (IOs) and enduring importance of incorporating actions in the information environment in military strategy.
Artificial intelligence (AI) and machine learning (ML) represent an increasingly exciting field of computer science. A term originally coined by John McCarthy in 1956,1 AI is becoming increasingly pervasive in today’s world.
FORT MEADE, Md. – The National Security Agency (NSA) joins the Federal Bureau of Investigation (FBI), the Cybersecurity and Infrastructure…
ALBUQUERQUE, N.M. — Sandia National Laboratories and Arizona State University, two research powerhouses, are collaborating to push the boundaries of…
U2opia Technology has licensed Situ and Heartbeat, a package of technologies from the Department of Energy’s Oak Ridge National Laboratory…
The director of the National Security Agency said the agency’s new Artificial Intelligence Security Center is paying dividends in the…
FORT GEORGE G. MEADE, Md. – Mr. Michael Clark, deputy director of plans and policy at U.S. Cyber Command, presented a…
FORT MEADE, Md. – The National Security Agency (NSA) is launching its annual Codebreaker Challenge, offering students from U.S.-based academic…