Identifying the most critical data and identities is important for a Zero Trust approach. A more robust security posture begins by understanding the organization’s security architecture before integrating controls and signaling across layers to apply and enforce unified policies. The Zero Trust architecture extends throughout the entire digital estate and serves as an integrated, unified security strategy to reduce the complexity and time-consuming aspects of end-to-end security.
Organizations must first gain visibility into what assets—such as identities, endpoints, apps, networks, infrastructure, and data—exist within their organization. Then, assess their current risk and identify which assets should be prioritized and which ones users are interacting with.
Securing sensitive data must involve these key steps:
- Gaining visibility into the existence (across multicloud, on-premises, and hybrid environments) and risks associated with how sensitive data is being used, accessed, and shared through built-in, ready-to-use machine learning models.
- Understanding insider risks by gaining insight into how users are interacting with sensitive data and leveraging sequence detection to understand user intent.
- Preventing data loss by preventing sensitive data from unauthorized use across apps, services, and devices.
- Leveraging dynamic controls to adjust data loss prevention policies to address the most critical data risks.
These steps enable organizations to adopt a comprehensive end-to-end strategy to manage security and apply protection actions—such as encryption, access restrictions, and visual markings—that safeguard your data, even if it leaves the devices, apps, infrastructure, and networks that the organization controls.
When data and sensitive content is understood, classified, and identified, organizations can:
- Inform and enforce policy decisions to block sharing of emails, attachments, or documents that contain sensitive data.
- Encrypt files with sensitivity labels on device endpoints.
- Auto-classify content with sensitivity labels through policy and machine learning.
- Detect sensitive data that travels inside and outside your digital estate and understand user context to better investigate and mitigate risks.
Fine-tuned adaptive access controls, such as requiring multifactor authentication or device security policies, based upon user context, device, location, and session risk information, move the security perimeter to where data lives and encourage strict control over digital identities and identity access. This enables the implementation of security controls within each layer of the security architecture to further segment access.
Policies and real-time signals are required to determine when to allow, block, or limit access, or require additional proofs like multifactor authentication so that organizations can improve boundaryless collaboration without putting their data at risk.
By adopting Zero Trust, organizations understand the context of user activity around sensitive data and can prevent unauthorized use or loss of data. Types of data security that help protect against data breaches and help meet regulatory requirements include:
- Data loss prevention to guard against unauthorized use of sensitive data.
- Encryption to make files unreadable for unauthorized users.
- Information protection to help classify sensitive data found in files and documents.
- Insider risk management to mitigate potentially risky user activity that may result in a data security incident.