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Cybersecurity researchers are warning about the security risks in the machine learning (ML) software supply chain following the discovery of more than 20 vulnerabilities that could be exploited to target MLOps platforms. These vulnerabilities, which are described as inherent- and implementation-based flaws, could have severe consequences, ranging from arbitrary code execution to loading malicious datasets. MLOps platforms offer the ability to design and execute an ML model pipeline, with a model registry acting as a repository used to store and version-trained ML models. These models can then be embedded within an application or allow other clients to query them using an API (aka model-as-a-service). "Inherent vulnerabilities are vulnerabilities that are caused by the underlying formats and processes used in the target technology," JFrog researchers said in a detailed report. Some examples of inherent vulnerabilities include abusing ML models to run code of the attacker

Event Overview The " AI Leaders Spill Their Secrets " webinar, hosted by Sigma Computing, featured prominent AI experts sharing their experiences and strategies for success in the AI industry. The panel included Michael Ward from Sardine, Damon Bryan from Hyperfinity, and Stephen Hillian from Astronomer, moderated by Zalak Trivedi, Sigma Computing's Product Manager. Key Speakers and Their Backgrounds 1. Michael Ward Senior Risk Data Analyst at Sardine. Over 25 years of experience in software engineering. Focuses on data science, analytics, and machine learning to prevent fraud and money laundering. 2. Damon Bryan Co-founder and CTO at Hyperfinity. Specializes in decision intelligence software for retailers and brands. Background in data science, AI, and analytics, transitioning from consultancy to a full-fledged software company. 3. Stephen Hillion SVP of Data and AI at Astronomer. Manages data science teams and focuses on the development and scaling of

Want to know what's the latest and greatest in SecOps for 2024? Gartner's recently released Hype Cycle for Security Operations report takes important steps to organize and mature the domain of Continuous Threat Exposure Management, aka CTEM. Three categories within this domain are included in this year's report: Threat Exposure Management, Exposure Assessment Platforms (EAP), and Adversarial Exposure Validation (AEV). These category definitions are aimed at providing some structure to the evolving landscape of exposure management technologies. Pentera, listed as a sample vendor in the newly defined AEV category, is playing a pivotal role in increasing the adoption of CTEM, with a focus on security validation. Following is our take on the CTEM related product categories and what they mean for enterprise security leaders. The Industry is Maturing CTEM, coined by Gartner in 2022, presents a structural approach for continuously assessing, prioritizing, validating, and remediating expo

Imagine a pocket-sized hard drive capable of storing the entire list of 35 Million Songs? This isn't yet practical, but IBM has just taken a big step towards improving computing technology: IBM researchers just discovered a way to store data on a single atom. Data storage is undergoing dramatic evolution, recently researchers successfully stored digital data — an entire operating system, a movie, an Amazon gift card, a study and a computer virus — in strands of DNA. The IBM Research results announced Wednesday that the researchers have developed the world's smallest magnet using a single atom and they packed it with one bit of digital data. Currently, hard drives use about 100,000 atoms to store a single bit of information — a 1 or 0 — using traditional methods. So, this breakthrough could allow people to store 1,000 times more information in the same amount of space in the future applications. Scientists Store 1 Bit of data on a single Atom, whereas modern hard dri