Architectural changes required: Analysts point out that meeting erasure deadlines and purpose-based storage limits will require deeper architectural changes.”Architectural changes include deploying encryption, masking, and tokenization for secure storage, implementing consent managers, and integrating erasure standards like NIST 800-88 or IEEE 2883 for IT asset sanitization,” Mahapatra said. “Cloud-native architectures with granular data classification and retention policies will become essential, along with real-time monitoring and backup deletion protocols to ensure compliance across distributed environments.”Grover noted that enterprises will need a stronger privacy-by-design architecture built on data discovery and classification tools, encryption, tokenization, and automated deletion workflows that trigger when consent is withdrawn or the purpose expires.”IDC’s Asia/Pacific Security Study 2025 indicates that data privacy and regulatory management are already among the top challenges for enterprises deploying AI and modern digital systems, which signals that organizations will need platform-level automation rather than manual retention workflows,” Grover added.She said companies will move toward segregated personal data zones, purpose-linked storage buckets, and centralized consent orchestration so that erasure, minimization, and provenance can be enforced consistently across cloud, on-prem, and SaaS systems.
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