Cybersecurity
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Organizations require comprehensive contextual prioritization and automated remediation of security vulnerabilities to scale their limited human cybersecurity resources effectively. At the Zscaler event, Zenith Live, the company showcased this vision, illustrating a platform that employs large-scale data collection, contextual AI data analysis, and intelligent automated security control implementation to mitigate risk and advance toward a self-healing system.
To learn more, download the free brief, Zscaler Showcases Innovation With Contextual Analysis and AI-driven Remediation at Zenith Live.
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As organizations increasingly move workloads to the cloud and security responsibility becomes decentralized, the difficulty in maintaining visibility across network resources and configuration hygiene rises. AWS Shield network security director can help organizations understand network security in place and make needed changes to mitigate risk for better security and compliance.
To learn more, download the free brief, AWS Shield Network Security Director Provides Mapping for Contextual Understanding to Mitigate Risk and Speed Response.
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Demands for breakneck speed in application development certainly aren’t going away in increasingly competitive business environments. However, teams that do so at the expense of robust cybersecurity practices threaten the viability of their organization when a successful attack on any application can compromise the business. Recent research by Enterprise Strategy Group investigated how organizations are incorporating security into application development processes, especially amid rapid cloud-native application development conditions.
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IT professionals, application developers, and cybersecurity teams all play critical roles in ensuring timely and secure development and deployment of cloud-native applications. However, these teams do not always have the same perspective on the practices, roles, and tool strategies that will best accomplish those goals. Recent research by Enterprise Strategy Group revealed the differences in how these groups perceive DevOps and DevSecOps processes at their organizations today.
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Gaps in cybersecurity skill sets, coverage, processes, and technology continue to plague many organizations today, and enterprises across industries are looking to AI and automation solutions to bridge many of these gaps. However, despite rapid advancement of AI technologies, recent research by Enterprise Strategy Group found that many organizations plan to leverage managed services for the foreseeable future to close gaps and accelerate program development.
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Security operations is a core function of cybersecurity, requiring a combination of skilled people, refined processes, and scalable technologies. While once focused on more reactive security functions, modern security operations centers (SOCs) are increasingly responsible for more proactive security functions, including monitoring security posture and status, managing threats and exposure, and analyzing threat intelligence, while continuing to triage, investigate, and respond to suspicious or malicious behavior. Recent research by Enterprise Strategy Group investigated how the size of a SOC impacts the adoption of technologies such as GenAI, leveraging third-party services and increasing organizations’ spending to better support their security operations and fortify their security posture.
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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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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.
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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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