Hacker forums provide critical early warning signals for emerging cybersecurity threats, but extracting actionable intelligence from their unstructured and noisy content remains a significant challenge. This paper presents an unsupervised framework that automatically detects, clusters, and prioritizes security events discussed across hacker forum posts. Our approach leverages Transformer-based embeddings fine-tuned with contrastive learning to group related discussions into distinct security event clusters, identifying incidents like zero-day disclosures or malware releases without relying on predefined keywords. The framework incorporates a daily ranking mechanism that prioritizes identified events using quantifiable metrics reflecting timeliness, source credibility, information completeness, and relevance. Experimental evaluation on real-world hacker forum data demonstrates that our method effectively reduces noise and surfaces high-priority threats, enabling security analysts to mount proactive responses. By transforming disparate hacker forum discussions into structured, actionable intelligence, our work addresses fundamental challenges in automated threat detection and analysis.

When artificial intelligence (AI) technologies first debuted, they didn't live up to the hype -- not even close -- and left many early adopters disappointed. But that was then and this is now, and AI is finally catching the attention of more end users, changing the core value proposition of physical and electronic security. "Many early AI video solutions did disappoint customers with perspective limitations and false alarms," says Jason Burrows, sales director, IDIS America. "But today, a single camera can provide highly accurate analysis from more angles than ever before and deliver high-performance, even in challenging light conditions." He points out that, initially, most AI was offered as software or modules within VMS, which oftentimes entailed expensive integration services, software licenses and maintenance agreements.

Well-capitalized start-up seeks extremely talented Senior Data Scientist to help pioneer the development of remote sensing data science technology. You should have a propensity for understanding complex data sets and sources with an ability to extract and distill the data into actionable intelligence. You should be an expert in Python machine learning libraries like Scikit-Learn, Keras, PyTorch or Tensorflow. You should be highly credentialed (Masters or PhD from a respected university) and have both academic and industry experience solving difficult data science problems. You should be someone who is passionate about their career and wants to join a small and dedicated team of highly motivated developers and analysts.

Leading video content analytics software provider BriefCam has announced its edge analytics-powered solutions would be available on Axis cameras with built-in deep learning processing units. This would enable users to scale video content analytics to new architectures. Currently, BriefCam's edge analytics initiative complements its portfolio of on-premise and cloud solutions, by enabling greater freedom of choice for flexible deployment architectures through edge-based computing. At the time of this announcement, Gili Rom, VP Strategic Initiatives, BriefCam said, "The breadth and depth of BriefCam's portfolio of video analytics solutions deliver actionable intelligence for safety, security and greater business insights with freedom of choice for on-premises, cloud and now edge deployment architectures through its deep partnership with Axis Communications. Having BriefCam edge analytics enabled on the Axis Q1615 MK III, as the first Deep Learning Camera, greatly accelerates the respective companies' value to the customer."

Internet of Things (IoT), 3D printing, self-driving automobiles, nanotechnology, SMAC (Social, Mobile, Analytics and Cloud) and a lot more …businesses especially the manufacturing organisations are experiencing a downpour of disruptive tech trends in the business intelligence landscape. However, when it comes for manufacturing businesses to adopt these tech trends, there is a dire need for having a lucid understanding of the various tech trends in line with current gen's business ecosystem. IoT and Big data undoubtedly are enthralling tech concepts. The question is what do they have in store for manufacturing organisations? As digitisation takes the manufacturing arena by a storm, there seems to be an amazing potential to boost agility, responsiveness and embrace the quickly changing customers' demands. Here's how the trio of ERP, Big Data and IoT can deliver actionable intelligence: With Great Power Comes Great Stress!

Now, take the conversation to Twitter! Agree or disagree with this perspective on DataOps? Want to ask Kirk a question?

It has never been easier to measure and monitor business operations -- the amount of data available to organizations is staggering. Access to insight provides businesses with a clear competitive advantage, but many enterprises struggle to make sense of the seemingly endless reams of data at their disposal. To overcome hurdles with data literacy, smart businesses have embraced various business intelligence (BI) solutions to collect, aggregate, translate and present business information. An invaluable asset for enterprises worldwide, BI dashboards are data visualization tools that display the status of business analytics metrics, key performance indicators (KPIs) and other important data points on a single screen. To underscore the widespread adoption of BI, note that the global business intelligence market is projected to reach USD $147.19 billion by 2025.

Our thirst for data is great. Our desire to be connected, at all times, means that by 2025 an average connected person will interact with connected devices one interaction every 18 seconds. The stat includes using smart home security, smart TVs and more – nearly 4,800 times a day. But how is AI impacting surveillance data storage? As our world becomes increasingly connected, the vast amounts of data being created are also enabling us to refine and improve systems and processes. We are seeing this from security through to smart cities and AI.

Kirk Borne, principal data scientist at Booz Allen Hamilton, gave a keynote presentation at this year's Oracle Code One Conference on how the connection between emerging technologies, data, and machine learning are transforming data into value. Borne explained that with IoT, the value is not in all of the data produced, but instead in the contextual knowledge that sensors give us about the world. With IOT and context knowledge, the goal is to discover and deliver value from data. Value from data is achieved by going from understanding to actions. Emerging technological innovations like AI, robotics, computer vision and more, are enabled by data and create value from data.

Overview The goal of artificial intelligence is to enable the development of computers to do things normally done by people -- in particular, things associated with people acting intelligently. In the case of cybersecurity, its most practical application has been automating human intensive tasks to keep pace with attackers! Progressive organizations have begun using artificial intelligence in cybersecurity applications to defend against attackers. However, on it's own, artificial intelligence is best designed to identify "what is wrong." What today's enterprise needs to know is not only "what is wrong" in the face of a breach, but to understand "why it's wrong" and "how to fix it!"