How Business Enablement for Data Privacy Would Proliferate Cybersecurity in 2022?
According to Gartner Inc., by 2023, 65% of the world’s population will have its data covered under modern privacy regulations. In the U.S., the number of data breaches specifically has skyrocketed and the highest contributing factor behind this breach was security system complexity and cloud migration, as figured out in reports by Statista. Whatever be the enterprises’ digital Achilles’ heel, such incidents of a data breach are demanding sound cybersecurity practices and security controls.
There needs to be business enablement for data privacy and security by leaders. Just because these business complexities exist organizations must not simplify and streamline their operations thoughtlessly, but consciously and deliberately.
Tech in itself is not a satisfactory solution to simplified cybersecurity, it must be a concern for the entire business for data privacy in every function and every employee. Let’s check how business enablement would work for data privacy.
What Business Enablements Would Better Manage Cyber Risks?
Data privacy enablement should be positioned to address, enable, enhance, and preserve the entire lifecycle of data. Here are some business enablements for data privacy that must be taken care of:
Secure Data Collaboration: Partnerships and alliances for data are now sought because without disclosing the raw data companies can help each other with macroeconomic insights.
Creating a “clean room” is a much-protected way of secure data collaboration. It is a secure environment with a de-identified and aggregated data set, enabling the connection of distributed data across multiple platforms and parties. It is meant to protect private and proprietary data through obfuscation.
Privacy-Preserving Machine Learning (PPML): Companies that are adapting multiple products and services across different geographies are unlocking hyper-local AI/ ML, which brings substantial business benefits and improved customer engagement.
The current cloud computing adoption scenario for machine learning (ML) questions the security of data which gave rise to the Privacy-Preserving Machine Learning approach (PPML). This technique implies various methods like differential privacy, homomorphic encryption, and decentralized ML processes are some that prevent the privacy information from being exposed.
Secure Multi Party Computation: This method helps businesses ensure the security of their sensitive data without undermining their ability to gain insights from it.
The secure multi-party computation eliminates the tradeoff between data privacy and data utility and eliminates the risk of data breaches and misuses stemming from data collection.
In a complex business environment, organizational complexity is posing concerns of cybersecurity and privacy risk, because of which enterprises must simplify their operations and processes consciously and deliberately.
To know different business enablements on data privacy, check out our blog.
Conclusion:
Checking on Digital Supply Chain will Reconceptualize Data Privacy
In 2022, the digital supply chain (a set of processes that involve advanced technologies and better insights into the function of each stakeholder) is more vulnerable to attacks by cybercriminals. This is demanding new mitigation approaches that involve more deliberate risk-based vendor segmentation and scoring, security controls, and best practices to bring a shift in resilient-based efforts to get ahead in security regulations.
Techment Technology focuses on building data privacy and protection strategy and implementing appropriate safeguards while venturing into data science and engineering. For getting detailed insights on data handling procedures get our free consultation.
