#data streams

[cloudopspulse]

AI-Powered Compliance: Achieving Total Visibility and Control

In a landscape defined by rapid regulatory shifts and increasing data complexity, organizations are moving away from retrospective "check-the-box" audits toward real-time compliance monitoring. This transition is a strategic necessity, as traditional manual sampling is no longer sufficient to maintain oversight across global, cloud-based enterprises. By integrating artificial intelligence into the governance framework, businesses can shift from reactive detection to proactive prevention, identifying suspicious risk indicators the moment they emerge.

The core advantage of an AI-driven approach is the ability to analyze 100% of data streams with surgical precision. Unlike manual reviews, which are prone to human error and significant time lags, automated systems use advanced data processing to monitor digital footprints and document every action against evolving legal standards. This comprehensive coverage is essential for meeting the stringent requirements of frameworks like the GDPR or the EU AI Act, where a single missed error can lead to staggering financial penalties.

Furthermore, automated policy enforcement bridges the dangerous gap between detection and remediation. By codifying internal rules, these systems can instantly block unauthorized data transfers or high-risk transactions, providing users with immediate feedback. This "compliance-by-design" approach fosters a culture of accountability while ensuring that security remains a silent enabler of business speed rather than a bottleneck.

Perhaps most importantly, this model ensures a state of perpetual audit readiness. By maintaining a continuous, validated digital trail of all interactions, organizations can demonstrate maturity to regulators without the traditional "audit season" scramble. Ultimately, transforming compliance into a dynamic, data-driven asset allows leaders to mitigate risk, protect their brand reputation, and build a resilient foundation for long-term growth. This proactive stance ensures that the organization remains hyper-aware of its actions, turning regulatory adherence into a powerful competitive differentiator.

Read more

[finovohub]

Chainlink launches real-time US equities data streams on 37 blockchains

Chainlink has introduced a new product called Data Streams, which delivers live pricing data for major US equities and exchange-traded funds (ETFs) directly onto blockchain networks. According to an Aug. 4 statement, Data Streams is designed to offer live, low-latency data on major US stocks and ETFs, including popular assets like SPY, QQQ, NVDA, AAPL, and MSFT. These data streams are now live…

Chainlink launches real-time US equities data streams on 37 blockchains

[databuser]

Anybody wanna see me on Twitch?

Yeah, it's been a while since last time I was in that place.

Just remaking my set up for future streams. Just being experimental, you know?

See ya there!

Watch this video on Twitch Live

[georgeanntremblyblog]

[reportwire]

My Favorite Online Courses to Learn Data Structure and Algorithms in JavaScript

My Favorite Online Courses to Learn Data Structure and Algorithms in JavaScript

Hello Devs, if you are a JavaScript developer learning data structure and algorithms and looking for the best online course to learn algorithms and data structure in JavaScript, then you have come to the right place. In the past, I have shared free tutorials to learn JavaScript and data structure algorithms, and today, I will share the best courses to learn data structure and…

View On WordPress

[mystlnewsonline]

MENLO PARK, Calif | Facebook's election 'war room' takes aim at fake information

MENLO PARK, Calif | Facebook’s election ‘war room’ takes aim at fake information

MENLO PARK, Calif.  — In an otherwise innocuous part of Facebook’s expansive Silicon Valley campus, a locked door bears a taped-on sign that reads “War Room.” Behind the door lies a nerve center the social network has set up to combat fake accounts and bogus news stories ahead of upcoming elections.

Inside the room are dozens of employees staring intently at their monitors while data streams…

View On WordPress

[algeroth]

Can scientists learn to make 'nature forecasts' just as we forecast the weather?

Big data open-access publishing and other advances offer ecologists the ability to forecast events like pest outbreaks over days and seasons rather than decades. But scholars need to seize this opportunity.

Big data is driving many of the advances in ecological forecasting. Today ecologists have orders of magnitude more data compared to just a decade ago, thanks to sustained public funding for basic science and environmental monitoring. This investment has given us better sensors, satellites and organizations such as the National Ecological Observatory Network, which collects high-quality data from 81 field sites across the United States and Puerto Rico. At the same time, cultural shifts across funding agencies, research networks and journals have made that data more open and available.

Digital technologies make it possible to access this information more quickly than in the past. Field notebooks have given way to tablets and cell networks that can stream new data into supercomputers in real time. Computing advances allow us to build better models and use more sophisticated statistical methods to produce forecasts.

So far, though, ecological forecasting has not kept pace with advances in data and technology. In our article, we lay out a road map for accelerating the field by tackling the bottlenecks slowing us down.

Some of these bottlenecks are technical, such as better integrating the streams of data that are now available from many different sources, such as field studies, sensor networks and satellite observations.

Other challenges involve human choices. Ecologists need to spend more time engaged in two-way communication with stakeholders, rather than just pushing out the latest research to decision-makers. And we need better ways to transfer state-of-the-art research from universities to agencies and private industry.

Perhaps most limiting is that ecologists traditionally have not been taught forecasting concepts and methods. But as I have written, this situation is changing. There now are summer workshops and a growing number of university courses in ecological forecasting. Prediction is leading to new theories that aim to unify different parts of ecology.

At the dawn of numerical weather prediction in the 1950s, scientists at the National Weather Service faced a choice. They could either wait to start forecasting until the underlying research, models and tools improved, or proceed immediately with making forecasts and learn by doing. They chose the second path. It proved harder than expected – but had they waited, they likely would have failed because they would have missed a critical window when experts and agencies were willing to make major investments in this effort.

Up to now, ecologists have generally adhered to the first, more conservative path. But in this time of rapid environmental change, the societal need and technological capacity for forecasting have never been greater. The forecasts won’t always be right, especially as the field develops, but failure is part of learning. The time for ecologists to start forecasting is now.

[thebestparisyouhave]

Hi Tom, I am happy you are again fit and healthy, but may I know if you have informed your family and what was their reaction? Wishing you a nice day, Tom!

Hi, thank you!  I’m happy to be back on duty, fit and healthy again too.  

So yes, I sent a note to my family in the last data stream about what happened. At that point I was better, but not yet back on duty. We haven’t had another transmission yet to see what their reaction was, but I have to say, I’m glad the timing worked out. It would have been awful to have to have told them about my illness and not know the outcome… I might not have told them, to be honest. My mother especially would have taken it very hard.