XDR, or Extended Detection and Response, is an emerging security technology that is rapidly gaining popularity in the cybersecurity industry. It is a comprehensive security solution that offers a unified approach to threat detection, investigation, and response across multiple endpoints, networks, and cloud environments. In today's digital age, cyber threats are becoming increasingly sophisticated and diverse, making it difficult for organizations to detect and respond to them in a timely and effective manner. Traditional security solutions, such as antivirus software, firewalls, and intrusion detection systems, are no longer sufficient to protect against the complex and evolving threat landscape. It collects and correlates data from various sources, including endpoints, network devices, and cloud platforms, and applies advanced analytics and machine learning algorithms to identify suspicious activity and potential threats.

Will AI Take the Risk Out of Insurance, Leaving Nothing But Pure, Unadulterated Coverage? Artificial intelligence (AI) is a rapidly growing field that has the potential to revolutionize the insurance industry. As a result, 74% of insurance executives are planning to increase their investments in AI. AI can significantly improve the efficiency and effectiveness of group insurance. McKinsey estimates that across functions and use cases, AI investments can drive up to a whopping $1.1 trillion in potential annual value for the insurance industry.

Machine Learning (ML) has gained prominence in recent years because of its ability to be applied across scores of industries and solve complex problems effectively. Yet, research shows that nearly 90% of AI/ML models never actually make it into production or hit the market. The main challenge is that ML/AI models require huge volumes of high-quality, accurate, and timely data to be effective, but organizations have long been reluctant to share sensitive information due to security and privacy concerns. Personal data is becoming more pervasive, causing privacy concerns to grow. As a result, global data protection laws have become stricter, and organizations face increasingly higher noncompliance risks. Mitigating such concerns and taking AI/ML to the next level requires a new approach to collaboration -- secure collaborative learning.

To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Confidential computing focuses on potentially revolutionary technology, in terms of impact on data security. In confidential computing, data remains encrypted, not just at rest and in transit, but also in use, allowing analytics and machine learning (ML) to be performed on the data, while maintaining its confidentiality. The capability to encrypt data in use opens up a massive range of possible real-world scenarios, and it has major implications and potential benefits for the future of data security. VentureBeat spoke with Raluca Ada Popa about her research and work in developing practical solutions for confidential computing.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. San Francisco-based Opaque Systems, a company enabling collaborative analytics and AI for confidential computing, today announced it has raised $22 million in a series A round of funding. Confidential computing has been a game-changer for enterprises. It encrypts sensitive data in a protected CPU enclave or trusted execution environment (TEE), giving companies a way to move beyond policy-based privacy and security to safeguard their information in the cloud. However, with this level of encryption, which can only be unlocked with keys held by the client, multiple parties struggle to access, share, analyze and run AI/ML on the data in question.

The term "real time" has been infused throughout tech, from real-time stock picks to real-time pizza tracking. As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past. Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked. These companies promise to leave plodding batch-data processing for old-school business intelligence analysis in the dust. But whether every enterprise needs, wants or is ready to operate at a clip as fast paced as a Citibank, Uber or Amazon remains to be seen. Updating data every few days, every night or even every hour or so for business analysis using a typical batch processing approach "is like playing Monday morning quarterback," said Venkat Venkataramani, CEO and co-founder of Rockset, a company that provides a database for building applications for real-time data, analytics and queries.

Despite teething problems, artificial intelligence (AI) has become mainstream. In fact, it is more than mainstream. That is to say, no matter how enterprises set up their technology infrastructure, it seems unlikely they will remain competitive without AI. Based on a survey of 5,501 businesses globally, the report shows that one-third of companies are currently using AI in some way, while 43% are exploring it. While recent advances are making AI more accessible than ever, the survey found that a lack of AI skills and increasing data complexity are top challenges.

As your business continues to grow, IT infrastructure will experience a massive change in scale. If your company is on a growth trajectory, it's time to start thinking about how intelligent automation with AIOps can play a crucial role in your IT operations. If you are new to AIOps, this article is a perfect place to start. Know what AIOps is, why you should use it, and how it will help your business. Coined by Gartner, AIOps or Algorithmic IT Operations platform is a solution that uses smart algorithms (powered by AI and ML), where machines solve known IT issues and intelligently automate repetitive and mundane jobs. It refers to a multi-layered technology platform that automates and enhances IT operations.

Amazon Web Services' new visual data preparation tool for AWS Glue allows users to clean and normalize data with an interactive point-and-click visual interface without writing custom code. AWS Glue DataBrew helps data scientists and data analysts get the data ready for analytics and machine learning (ML) 80 percent quicker than traditional data preparation approaches, according to the cloud provider, which made the tool generally available on Wednesday. The new offering builds on AWS Glue, which AWS generally released in April of 2017. AWS Glue is a serverless, fully managed, extract, transform and load (ETL) service to categorize, clean, enrich and move data between various data stores. It has a central data repository called the AWS Glue Data Catalog, an ETL engine that generates Python code automatically and a flexible scheduler to handle dependency resolution, job monitoring and retries.

Inc. company announced the general availability of AWS Glue DataBrew, a new visual data preparation tool that enables customers to clean and normalize data without writing code. Since 2016, data engineers have used AWS Glue to create, run, and monitor extract, transform, and load (ETL) jobs. AWS Glue provides both code-based and visual interfaces, and has dramatically simplified extracting, orchestrating, and loading data in the cloud for customers. Data analysts and data scientists have wanted an easier way to clean and transform this data, and that's what DataBrew delivers, with a service that allows data exploration and experimentation directly from AWS data lakes, data warehouses, and databases without writing code. AWS Glue DataBrew offers customers over 250 pre-built transformations to automate data preparation tasks (e.g.