Amid the proliferation of sophisticated cyber attacks, network security remains an overarching component of an organization’s overall security strategy to detect and respond to threats. Network detection and response (NDR) tools have proven to support a diverse set of use cases, including improving response capabilities, accelerating incident response processes, detecting advanced attacks, and monitoring cloud environments. Security teams recognize artificial intelligence and machine learning (AI/ML) embedded in NDR tools are critical in providing more accurate and faster threat detection, as well as greater operational efficiency.

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Encrypted threats continue to pose a problem for many businesses. Nearly every organization decrypts and inspects some of its traffic, yet most companies refrain from decrypting all their traffic and therefore lack the visibility necessary to prevent encrypted attacks. Many enterprises use various tools and techniques in the hopes of increasing visibility with and without decryption. Network detection and response tools that detect encrypted threats without decrypting, help close visibility gaps, and prevent attackers from exploiting encrypted traffic are gaining wider attention.

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Facing pressure to do more work with fewer resources and a continuing cybersecurity skills shortage, organizations are looking to consolidate resources to drive more efficiencies in securing cloud-resident data while reducing overall risk. To secure data across hybrid environments, organizations are consolidating the efforts of on-premises and cloud data security teams. Multiple stakeholders, led by cloud security architects, create consistent security policies and determine security control requirements. Organizations also want an integrated platform that combines multiple security tools and controls and provides a global view of all organizational data. In the long run, controls tailored to secure data based on where it resides (on premises, SaaS, or IaaS/PaaS) will be used to account for the different techniques used across different environments.

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The complexity of cloud environments and the speed and scale of operations in the cloud drive the multitude of challenges organizations face in securing their cloud-resident sensitive data. The most difficult challenges include discovery and classification of data as well as ensuring compliance with regulations. Despite confidence in their data security tools, organizations continue to lose data due to misconfiguration, misclassification, and unknown (shadow) data. Implementing a defense-in-depth strategy that combines third-party and CSP-native tools and controls can overcome these challenges in securing cloud-resident sensitive data.

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The use of public cloud services (SaaS and IaaS/PaaS) has been increasing over the past several years. Subsequently, organizations have migrated more data assets to cloud stores. As organizations find that the amount of cloud-resident sensitive data is increasing, the challenge to sufficiently secure this data, especially when distributed across multiple clouds, becomes greater. In light of the disparate and native controls and policies provided by individual cloud service providers (CSPs), organizations need to craft a comprehensive, defense-in-depth strategy to adequately address the data security challenge.

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While a substantial percentage of organizations are aware of the loss of cloud-resident sensitive data, some organizations suspect they have lost data but do not definitively know. This lack of awareness indicates that organizations lack the tools or experience to confidently identify every data loss incident. As a result, organizations fail to learn from, respond to, and address the multiple causes of data loss, resulting in more incidents and greater monetary, regulatory, reputational, and existential risk.

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While the need to secure public cloud-resident data is critical, organizations’ confidence in the tools and controls provided by cloud service providers (CSPs) is lukewarm. To alleviate these concerns, organizations are using a combination of CSP-native and third-party controls to secure cloud-resident sensitive data. This defense-in-depth strategy provides a multi-layered approach to address multiple dimensions of data security.

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The role of the cloud security architect (CSA) has emerged to lead the charge in securing cloud-resident sensitive data. Yet, data security remains a responsibility shared by multiple groups including IT operations, security, and DevOps. The establishment of CSAs shows that securing cloud-resident data is of strategic importance, especially to cybersecurity, as the role now reports to the C-level, most often the CISO.

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Organizations are increasing investments in cyber-threat intelligence programs to get ahead of threat actors and cyber-attacks. Beyond traditional threat intelligence, firms are adopting digital risk protection (DRP) programs and/or services to safeguard the growing volume of digital assets. DRP encompasses a mix of traditional and emerging areas like mobile application protection, brand protection, executive protection, and deep/dark web monitoring.

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