Kiran Kumar NR’s Post

Learn best practices for integrating advanced AI technologies while maintaining robust security measures to protect your data. #Azure #AI #CyberSecurity #GenerativeAI #MicrosoftEntra #DataProtection #TechInnovation #CloudSecurity

Companies started moving their data to the cloud around 10-15 years ago, and in many ways, the current effect of Large Language Models (LLMs) on data security is even bigger and faster. Broadly speaking, we can say there have been 3 ‘eras’ of data and data security: 🔸 The Era of On-Prem Data 🔸 The Era of the Cloud 🔸 The Era of AI Unfortunately, we’re still dealing with the challenges from the cloud era... That's why I wrote the latest Sentra blog, to discuss how DSPM has adapted to solve these eras’ primary data security challenges. Learn more 👇

Beringer Technology Group published a blog today called: Secure Your Data For Microsoft 365 Copilot Did you know that while Microsoft Copilot makes it easier for your team to access information, it will expose your security holes? This blog shares our six recommended access control management and data protection policies, and shares how Beringer can digitally transform your business with the latest Microsoft cloud technologies. https://lnkd.in/eCZ6YkBk #microsoftcopilot #copilot #artificialintelligence #AI #microsoftazure #btgrocks

“But we recognise it’s not enough to have strong foundational infrastructure for our own AI security,” Venables said. “We have to empower customers to manage AI safely and securely in their environments.” Comments from Phil Venables from the recent SICW/GovWare 2024 event. The below article defines Google Cloud's approach to safe and responsible AI with a secure AI framework that is integrated to equip businesses with tools and guidance to better manage the risks associated with AI deployments. https://lnkd.in/gSCmy5k9

Today, Baffle introduced new capabilities to secure unstructured data at the file or field level, addressing the complexity of various file formats across multiple locations. With these capabilities, your data can be moved to and within the cloud while meeting all compliance requirements globally for analytics and AI https://lnkd.in/gBV9VtPE

How to take your Sentinel Log Collection strategy to the next level.  In the ever-evolving world of cybersecurity, staying one step ahead of threats is not just a goal, but a necessity. This is where a robust SIEM (Security Information and Event Management) strategy becomes invaluable. But how can we manage the large amount of log volumes? This is where Azure Data Explorer and Event Hub come in—a dynamic duo that redefines the boundaries of what is possible in cybersecurity analytics. Azure Data Explorer is a lightning-fast service optimized for analysing large volumes of diverse data. This can be a game-changer for SIEM, offering unparalleled speed and efficiency in data analysis. Whether it's logs, traces, or web data, Azure Data Explorer processes and analyses it in real time, providing actionable insights faster than ever. Event Hub, on the other hand, is a highly scalable data streaming platform. It's designed to ingest and process large streams of data events, making it an ideal partner for Azure Data Explorer in handling massive amounts of security data. Together, they create a seamless data ingestion and analysis pipeline, ensuring that your SIEM strategy is proactive and reactive. Why is this combination a must-have for your Sentinel strategy? 1. Enhanced Real-Time Analysis: Immediate detection and response to potential threats become possible, minimizing the risk of significant impact. 2. Scalability and Flexibility: No matter the volume or velocity of your data, this setup scales accordingly, ensuring you are always covered. 3. Cost Efficiency: By optimizing the processing and analysis of data, companies can significantly reduce overhead costs associated with data management. 4. Avoid service barriers: KQL can be used to search through logs in Log Analytics, Event Hubs, and Data lakes.... Here is an exemplary structure: https://lnkd.in/e8dW8FV4 My Thoughts: Many companies connect sources with high log volumes directly to Sentinel. This often generates high costs and brings little added value. Log sources should be evaluated and, depending on the application purpose, the logs can be moved to a data lake and searched by ADX. PS: How are you leveraging Azure Data Explorer and Event Hub to revolutionize your SIEM strategy? Share your success stories and challenges! #CyberSecurity #EventHub #Sentinel #DigitalDefense #InfoSec

A unified data security governance strategy can help ensure consistent controls at scale for the next generation of AI applications that will be built on diverse data assets and cloud services. This article provides a few considerations for building out a unified data security strategy on your own. #data #strategy #security

More than 90% of organizations use multiple cloud infrastructures, platforms, and services to run their business, adding complexity to securing all data.1 Microsoft Purview can help you secure and govern your entire data estate in this complex and changing environment. #Microsoft #Purview #data #security

The chat on 'SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective' by Dr Anton Chuvakin Timothy Peacock Travis Lanham at the Google Cloud Security Podcast by Google was super helpful. Learned a bunch of things I didn't know! Topics: • There’s been a ton of discussion in the wake of the three SIEM week (https://lnkd.in/gSZ55Uik) about the future of SIEM-like products. We saw a lot of takes on how this augurs the future of disassembled or decoupled SIEMs (https://lnkd.in/g5SkuNC8...) . Can you explain what these disassembled SIEMs are all about? • What are the expected upsides of detaching your SIEM interface and security capabilities from your data backend? • Tell us about the early days of SecOps (https://lnkd.in/g5SkuNC8...) (nee Chronicle) and why we didn’t go with this approach? • What are the upsides of a tightly coupled datastore + security experience for a SIEM? • Are there more risks or negatives of the decoupled/decentralized approach?  Complexity and the need to assemble “at home” are on the list, right? • One of the 50 things Google knew to be true back in the day was that product innovation comes from technical innovation, what’s the technical innovation driving decoupled SIEMs? • So what about those security data lakes? Any insights? Podcast link: https://lnkd.in/gE-JF2HY

Snowflake Data Clean Rooms helps organizations preserve the privacy of their data: Snowflake introduced Snowflake Data Clean Rooms to customers in AWS East, AWS West, and Azure West, revolutionizing how enterprises of all sizes can securely share data and collaborate in a privacy-preserving manner to achieve high value business outcomes in the Data Cloud. The general availability follows Snowflake’s acquisition of data clean room technology provider Samooha, which was named one of the most innovative data science companies of 2024 by Fast Company. Samooha is now integrated … More → The post Snowflake Data Clean Rooms helps organizations preserve the privacy of their data appeared first on Help Net Security. @Poseidon-US #HelpNetSecurity #Cybersecurity