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  • Saturated Application Environments Push AI-led Observability to Its Early Limits

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    Torsten Volk

    Organizations are constantly developing and deploying new applications in an effort to supercharge business processes, enhance employee productivity, deliver unique customer experiences, and more. As a result, many enterprises have ended up with a massive application portfolio. Recent research by Enterprise Strategy Group investigated how application volumes can affect an organization’s observability needs now and in the future.

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  • Industries Dive Headfirst Into AI-enhanced Observability

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    Torsten Volk

    Observability vendors are in a race to leverage AI to automate root cause analysis, enable self-healing, optimize resources, reduce alert noise, automate log analysis, and deliver contextualized actionable insights to end users. Organizations across industries recognize that implementing AI-enhanced observability tools can give them strategic insights that optimize the economics of their application development and platform engineering practices. However, Enterprise Strategy Group’s recent research reveals that an organization’s industry significantly influences three key aspects of AI-enhanced observability: the specific operational benefits realized, perceived return on investment, and how frequently teams override AI recommendations.

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  • Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era

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    Todd Thiemann

    Enterprises need to provide access to sensitive data while controlling against the unauthorized disclosure of that information from inadvertent leakage, insider threats, and outside attacks targeting data. Work-from-home and bring-your-own-device initiatives pose increased data loss prevention (DLP) challenges, and generative AI (GenAI) has opened new avenues for data leakage. Although DLP is a top investment category when it comes to data security, enterprises continue to struggle to classify data and control against data loss. Enterprise Strategy Group recently surveyed IT and cybersecurity professionals to gain insights into these trends.

    To learn more, download the free infographic, Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era.

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  • Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era

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    Todd Thiemann

    Enterprises need to provide access to sensitive data while controlling against the unauthorized disclosure of that information from inadvertent leakage, insider threats, and outside attacks targeting data. Work-from-home and bring-your-own-device initiatives pose increased DLP challenges, and new collaboration platforms and GenAI applications have opened new avenues for data leakage. Additionally, the proliferation of cloud services poses threats for data exfiltration, while intellectual property and trade secrets take new forms that do not lend themselves to conventional DLP solutions.

    Although DLP is a top investment category when it comes to data security, enterprises continue to struggle to classify data and control against data loss. Whether an enterprise DLP solution or DLP functionality within another security technology, current offerings generate considerable false positive alerts that distract teams that must evaluate and respond to such alerts. Existing approaches relying on regular expression (regex) rules can be brittle and require considerable maintenance, while current DLP solutions frequently encounter scaling and performance issues. Furthermore, complex data types like software code or health sciences data can be difficult to categorize.

    To gain insights into these trends, Enterprise Strategy Group surveyed 370 IT and cybersecurity professionals in North America (U.S. and Canada) involved with identity security technologies and processes.

    Already an Enterprise Strategy Group client? Log in to read the full report. If you are not yet a Subscription Client but would like to learn more about accessing this report, please contact us. 

  • Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era

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    Todd Thiemann

    This Complete Survey Results presentation focuses on how organizations categorize and protect data and control against data loss across the enterprise attack surface, which includes the challenges of preventing unauthorized disclosure of sensitive data, the risk posed by today’s data loss prevention (DLP) solutions, and the impact of cloud services and generative AI technologies.

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  • Messaging on AI-enabled Endpoint Benefits Needs to Mature

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    Gabe Knuth

    The messages from vendors in the early days of AI PCs were focused on concepts like audio and video enhancement, real-time translation, and accessibility features (e.g., sign language interpretation, gesture-based controls, etc.). As the AI PC market matures, new light is shone on the success factors that organizations need to see to increase adoption, and recent research by Enterprise Strategy Group found that those features go far beyond basic AI functionality and value.

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  • ARM-based Windows Devices Are Getting Serious Attention

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    Gabe Knuth

    The allure of Windows PCs equipped with ARM processers has been tantalizing for many years, especially with the rise of more powerful smartphones. The desire reached a fever pitch when Apple switched to an ARM architecture for its laptops in 2020, which enabled it to boast unheard of battery life and performance numbers. Since then, Microsoft (and hardware vendors) have been looking to diversify their offerings. Recent research by Enterprise Strategy Group investigated the current interest levels in ARM-based Windows devices as well as the benefits and challenges organizations expect to experience from utilizing them.

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  • The State of DevSecOps and Cloud Security Platforms

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    Melinda Marks

    As organizations today face pressure to boost their productivity and scale while efficiently optimizing resources, they are increasingly utilizing cloud services to deliver cloud-native applications. In recognition of the impact of security incidents on their cloud-native applications, cybersecurity teams need to look for ways to gain unified visibility and control to efficiently manage risk and rapidly respond to attacks by incorporating security into DevOps processes (DevSecOps) and utilizing cloud security platforms. Enterprise Strategy Group recently surveyed IT, cybersecurity, and application development professionals to gain insights into these trends.

    To learn more, download the free infographic, The State of DevSecOps and Cloud Security Platforms.

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  • Tool Consolidation and AI Push Cybersecurity Data Fabrics Forward

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    Tyler Shields

    Today’s cybersecurity teams encounter issues such as fragmented tools, siloed data, and increased operational complexity, reducing their effectiveness in managing business and technology risks. Recent findings from Enterprise Strategy Group show a shift toward tool consolidation and the integration of cybersecurity data security fabrics and comprehensive platforms to tackle these challenges. This brief examines how consolidation and the emergence of AI capabilities are pushing the adoption of data fabrics.

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  • State of DevSecOps and Cloud Security Platforms: Scaling Security Practices to Accommodate Cloud-native Application Development

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    Melinda Marks

    This Complete Survey Results presentation focuses on the scope of cloud-native application development environments, including the top challenges associated with securing cloud-native applications and the security solutions in place to protect cloud infrastructure and applications.

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  • State of DevSecOps and Cloud Security Platforms: Scaling Security Practices to Accommodate Cloud-native Application Development

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    Melinda Marks

    As organizations today face pressure to boost their productivity and scale while efficiently optimizing resources, they are increasingly utilizing cloud services to deliver cloud-native applications. Cybersecurity teams recognize the impact of security incidents on their cloud-native applications, including application downtime, business disruption, compliance fines, and negative brand reputation. They need effective cybersecurity solutions that address security risk from development to deployment to ensure security teams can support business growth.

    The efforts to modernize application development utilizing cloud services are focused on optimizing efficiency for growth and scale. However, having separate, siloed security tools that work in different parts of the software development lifecycle works against the speed and efficiency that organizations are trying to achieve. As a result, organizations need to look for ways to gain unified visibility and control to efficiently manage risk and rapidly respond to threats and attacks by incorporating DevSecOps and utilizing cloud security platforms.

    To gain insights into these trends, Enterprise Strategy Group surveyed 373 IT, cybersecurity, and application development professionals in North America (U.S. and Canada) responsible for evaluating or purchasing cloud security technology products and services.

    Already an Enterprise Strategy Group client? Log in to read the full report. If you are not yet a Subscription Client but would like to learn more about accessing this report, please contact us. 

  • IBM Aims to Boost AI Inferencing With ‘Content Aware’ Storage

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    Simon Robinson

    IBM made a number of announcements recently that highlight its ongoing commitment to help customers accelerate their AI journeys. Of particular note is a pending capability developed by IBM Research that adds ‘content awareness’ to both its own and third-party storage, which when integrated with the NVIDIA Data Platform will bring vector processing closer to the storage layer to substantially improve the effectiveness of RAG-based inferencing.

    To learn more, download the free brief, IBM Aims to Boost AI Inferencing With ‘Content Aware’ Storage.

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