With these approaching threats, companies need to take the concept of data privacy and security in 2025 in 2025. This starts with seeing privacy as a core business function, not check -off boxes. Instead of regarding privacy as add -ons, invading business in business allows you to fully visualize your organization’s data stack, ensuring better control and protection of enterprise data used for AI.
Privacy and security first concept
Standing data privacy as a business process does not happen overnight. Organizations need to take several measures to effectively shift to this concept.
The first step that a company should take is to define a comprehensive data strategy, for example, a plan to manage data in a hybrid cloud environment. The organizational data strategy, especially when using AI, is exactly how to observe the “Privacy by Design Privacy” approach.
First of all, “privacy bi -design” refers to frameworks that actively incorporate privacy in information technology, network infrastructure, and business practical design specifications. There are seven basic principles to do this effectively.
Not a reactive, but an aggressive: Privacy by design predicts risk and prevents privacy infringement before it occurs. It comes “before the fact, but before the facts.” Default Privacy: Designed privacy guarantees that personal data is automatically protected by IT system or business practices as a default. In other words, privacy is built into the system. Privacy built into the design: Privacy measurement must be an essential component of the core function, rather than bolt -on as an add -on of IT systems and business practices and architecture. Privacy with positive -design, not a complete function -positive -design, avoid two -minute methods, such as privacy and security, which generates unnecessary trade -offs. It indicates that it is possible to have both. End -to -end security -full life cycle protection: Privacy by design extends security throughout the life cycle of relevant data, from collection and use to destruction or deletion. It is incorporated into the design of the IT system from the beginning. Visuality and transparency: The “Verification of Trust” approach guarantees that the design of the design is fully recognized that the data is collected and why it is completely recognized. All component parts remain transparent for users and providers. Collected and saved data requires an effective purpose to benefit customers. Respect for User Privacy: User -centered privacy goals include strong privacy defaults, appropriate notifications, and authorized user -centered options, and the architect and operator prioritize individual interests. You need to attach it.
When “Privacy by Design” is established, the organization works through data audits, analyzes the data stored by the organization, where it is stored, how to use, when, when, when, who has permission. You need to make it clear. How to delete or mask the data from the data entity for use and the request. These steps are important in the AI era. You need to attach a label to the data, and to determine whether it can be used safely within the AI model, you need to obtain the correct access permit.
In order to enhance this approach, many companies are modernizing data architectures to support governance efforts. The most robust solution is designed in multiple security layers to protect various threats, such as unauthorized access, data infringement, and cyber attacks. Suppose the organization does not have the most efficient and latest data architecture. In that case, these expanded protection protocols, such as data encryption, multi -factor authentication, data masking, audit logs, and disaster recovery plan, cannot be developed. These are all useful for AI. -Storious cyber attacks or accidental mistakes in data in AI systems. Furthermore, this type of architecture allows companies to bring AI to data, by adding AI extensions to data platforms and reducing the risk of misuse using AI models.
Also, this architecture guarantees that all company data exists in one center, regardless of format or structure. In particular, the hybrid data lake shop provides intensive security management, enables consistent security policy throughout the dataset, implements data access, encryption, and audit, reducing the possibility of security gaps. You can. In addition, it can be integrated with the robust data governance framework so that the organization can implement strict controls on data system, ownership, and use. The advantage provided by this type of architecture is to enhance security by ensuring that only certified data owners can change or use data and that all actions can be traced.
