What is data protection, and why does it matter?
The process of protecting data and recovering crucial information in the event that it is lost, damaged, or compromised as a result of cyberattacks, system failures, deliberate damage, or human mistake is known as data protection. It encompasses the tools, procedures, workflows, and policies that provide legitimate access to data, making it accessible when required.
Proper data protection may rely on a variety of technologies and strategies to safeguard data and guarantee access:
- Storage arrays, storage servers, and storage systems using solid-state or magnetic drives.
- High-availability strategies, ongoing data protection, and conventional data backups.
- Tiering storage for data that is more often accessed or of greater importance.
Data protection must be backed by data inventory, data backup and recovery, and a plan to manage the data across its lifespan in order to guarantee that it is kept and managed appropriately:
- The enterprise’s data types and quantities are determined by the data inventory, which also guarantees that all identified data is included in data protection planning and lifecycle management.
- Backup and recovery monitors backup frequency, describes the data recovery procedure, and protects data against hardware failures, unintentional loss, and deliberate malicious activity.
- Data lifecycle management refers to the procedures and instruments used to monitor the classification, storage, protection, and ultimate destruction of data in accordance with industry standards, privacy legislation, and corporate data protection policies.
What is data protection crucial?
Every day, all of us together produce over 2.5 quintillion (million trillion) bytes of data. Businesses’ commercial destiny is largely determined by how they gather, handle, store, and monetize this data. It is crucial to set up procedures and put technology in place to safeguard this important asset’s integrity and legitimate access.
It’s not a simple process. The environment around data security is far more complicated and different now than it was a few years ago. There are several privacy and data protection issues. The vice president of cybersecurity consulting at managed security services company Nuspire, Mike Pedrick, said, “In industry circles, consumer data is often compared to plutonium—powerful and valuable but terribly dangerous to the handler if abused.”
The following are some of the major problems that companies and their data security teams deal with on a regular basis:
- Handling, keeping, and making money off of enormous volumes of data that have been gathered.
- Figuring out when data has outlived its usefulness and turned into a burden.
- Preventing data breaches and more complex cybersecurity threats.
- Protecting files and data in cloud settings that are becoming more dispersed.
- Incorporating the newest technology into business and IT infrastructures that already exist.
- Using machine learning, AI, and now generative AI technologies to their fullest potential.
- observing the most recent revisions to national and international privacy and data protection regulations.
- Adjusting to more stringent and sometimes vague regulations that have harsh consequences.
- Adjusting to shifting expenditures and financial concerns brought on by global events that are beyond most firms’ control.
How data privacy is enhanced by GDPR compliance
Following the General Data Protection Regulation (GDPR) entails more than just avoiding harsh fines and pleasing authorities for businesses operating in EU nations. The goals and principles of the GDPR compel businesses to implement internal policies and processes that can enhance data protection initiatives in a number of crucial areas, including cloud migration, business continuity, data stewardship and governance, data backup and recovery, data monetization, and transparency and discoverability. There are companies that provide GDPR service that is designed to seamlessly integrate the standards into business processes and technologies across multiple companies.
The following factors make these sectors crucial to data protection:
- Improved business continuity raises the likelihood that companies will be able to promptly recover vital systems and resume operations after a data breach.
- Finding, processing, safeguarding, and securing data is accelerated by a well-defined data governance plan, discoverability, and transparency features. These features also increase the process’s scalability to optimize and profit from data resources.
- Companies that abide by the GDPR show authorities, clients, and partners showing they are serious about protecting data and they are good stewards of personal information, which might boost their brand’s credibility and provide them an advantage over rivals.
GDPR fails to provide enough advice on AI
Early on, there were concerns about whether the GDPR’s laws would be enforced due to their vagueness and absence of a centralized enforcement body. When the GDPR imposed many penalties on significant international corporations, all uncertainties were eliminated:
- In 2023, Meta received a record-breaking $1.3 billion punishment for sending personally identifiable information (PII) across international boundaries without sufficient data safeguards.
- In 2021, Amazon was penalized for utilizing customized advertising without getting customers’ permission.
- In 2023, TikTok was penalized for failing to comply with the GDPR’s standards for data processing and transparency.
- Between 2019 and 2022, Google received many fines, mostly for its ad-personalization products’ inadequate consent and transparency.
But with businesses facing harsh fines for using AI, machine learning, and generative AI in the gathering, processing, keeping, and sharing of personal data, the problem of the GDPR’s vagueness has surfaced again.
Sophie Stalla-Bourdillon, a senior privacy consultant and legal engineer at data security platform provider Immuta, said that when companies are relentless in their desire to compete in the AI race despite the risks, the GDPR’s principled approach becomes less effective in guiding practices. Additionally, Davi Ottenheimer, vice president of trust and digital ethics at data infrastructure software company Inrupt, said that if you imagine a robot that can only be switched off but not reprogrammed, you see the problem with AI and the GDPR.
Tom Moore, senior managing director of the consulting firm Protiviti, claims that:
- The problem of algorithmic bias in training data is not specifically addressed by the GDPR.
- The complexity of AI supply chains and who has responsibility when damage happens and various parties are engaged are issues that the GDPR does not adequately address.
- Beyond data protection, the GDPR does not specifically address the larger ethical and cultural concerns surrounding AI.
- Risks and issues unique to a certain sector are not covered by the GDPR.
The newly enacted “AI regulatory framework,” the Artificial Intelligence Act of the European Union, attempts to define terminology like high-risk AI systems, general-purpose AI systems and models, and other AI systems. Industry experts will wish to collaborate with their advisers to help evaluate the law’s consequences until and after the authorities reveal implementation specifics.