Published: 08 April 2024
数据安全平台（DSP）将数据安全控制与业务逻辑和细粒度授权相结合，可显著提升数据安全措施的成效、确保数据的安全性。安全和风险管理领导者可阅读本文，了解DSP市场的定义、运行机制和动态。
多数企业机构采用的数据安全策略和框架已经过时。同样过时的还有仅专注于发现攻击者的安全工具，这些工具缺乏充足的数据防护能力，难以支持高级用例的内外部数据共享需求。
企业机构的IT环境充斥着孤岛式的安全控制措施，导致其难以针对常见用例实施统一的数据安全策略。
数据策略要素和规则的激增，促使厂商加快步伐，在其数据安全平台（DSP）中引入各种不同的数据安全功能。
部署了DSP的企业机构正在精简其数据访问控制和防护措施，这有助于提高数据安全的可观测性，改善数据安全和监管合规。
负责大型数据存储的安全和风险管理领导者应：
借续签合同的机会对相关供应商和产品进行整合，降低数据安全工作的复杂度和成本。例如，可选择将数据库活动监控、数据脱敏、数据发现和标记化产品整合到DSP中。
优先选择可支持多种数据存储类型和业务应用的DSP产品，以最大限度提升部署的灵活性。
在设计（新的）数据存储时，将DSP作为核心（而非事后）考虑因素。此类数据存储可能包括，用于储存人工智能（AI）和机器学习（ML）用例所需原始数据的数据湖。
制定基于策略的数据访问规则，确保针对不同数据孤岛实施标准化的数据安全控制，改善数据安全治理和安全态势，并且支持业务部门的数据共享需求。
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Analysts:
Joerg Fritsch, Brian Lowans, Andrew Bales
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