Data Storage in Healthcare

The healthcare industry deals with vast amounts of sensitive and confidential patient information. To effectively store and manage this data, healthcare organizations employ various methods, including:

Electronic Health Records (EHR): EHR systems enable the digital storage and management of patient medical records, test results, and treatment histories. These records are stored securely in electronic databases, accessible only to authorized healthcare professionals.

Cloud-Based Storage: Many healthcare providers are adopting cloud-based storage solutions like Google Cloud, and AWS to store and back up their data. Cloud platforms offer scalability, accessibility, and disaster recovery capabilities while adhering to stringent security measures.

Data Warehousing: Healthcare organizations often utilize data warehouses to consolidate and analyze vast amounts of patient data. These warehouses ensure efficient data storage, integration, and retrieval for research, analytics, and decision-making purposes.

Data Security in Healthcare

To safeguard patient information and maintain data integrity, healthcare providers implement robust security measures:

Access Controls: Healthcare organizations employ strict access controls, limiting data access to authorized personnel only. User authentication mechanisms, such as usernames, passwords, and two-factor authentication, are implemented to prevent unauthorized access.

Encryption: Sensitive data is encrypted both during transmission and storage to protect it from unauthorized interception or access. Encryption techniques like Secure Sockets Layer (SSL) or Transport Layer Security (TLS) are commonly used.

Data Assurance Services continue to be a top priority in the healthcare and BFSI sectors, where the stakes are high in terms of privacy breaches and regulatory non-compliance.

Data Loss Prevention (DLP): DLP technologies such as Microsoft Security, help prevent accidental or intentional data breaches by monitoring and controlling the transfer of sensitive data within and outside the organization. These tools can identify and block unauthorized data transfers, ensuring compliance with data protection regulations.

Compliance Measures in Healthcare

Healthcare organizations must adhere to various compliance requirements to ensure data protection and privacy:

Health Insurance Portability and Accountability Act (HIPAA): HIPAA sets standards for protecting sensitive patient information, known as Protected Health Information (PHI). Compliance involves implementing physical, technical, and administrative safeguards to secure PHI and training employees on privacy practices.

General Data Protection Regulation (GDPR): Although primarily applicable in the European Union, GDPR has an extraterritorial impact on healthcare organizations globally. It mandates the protection of personal data and grants individuals’ control over their data, requiring organizations to implement robust security measures and obtain informed consent.

Data Storage in BFSI

Similar to healthcare, the BFSI sector handles vast amounts of sensitive financial and customer data. Data storage methods employed in BFSI include:

Core Banking Systems: BFSI organizations typically have core banking systems that store customer account information, transaction history, and other financial data securely. These systems are designed with redundancy and backup mechanisms to ensure data availability.

Data Centers: Many BFSI organizations maintain their own data centers, equipped with state-of-the-art infrastructure and security measures. These data centers provide a controlled and secure environment for storing and managing critical data.

Data Security in BFSI

The BFSI industry faces constant cybersecurity threats, and securing financial data is crucial. Security measures employed in BFSI include:

Network Security: To safeguard against unauthorized access and data breaches, it is crucial to have strong network security measures in place, including reliable firewalls, intrusion detection and prevention systems, and a secure network infrastructure.

Encryption and Tokenization: Sensitive data, such as customer financial details and authentication credentials, is encrypted to prevent unauthorized access. Tokenization techniques replace sensitive data with non-sensitive equivalents, further enhancing security.

Compliance Measures in BFSI

The BFSI industry is subject to numerous compliance regulations to safeguard customer data and maintain the integrity of financial systems. Several important compliance measures include:

The Payment Card Industry Data Security Standard (PCI DSS) lays out security standards for organisations that deal with credit cardholder data. In order to comply, one must maintain a secure network, put in place strict access rules, frequently check and test security systems, and more.

Anti-Money Laundering (AML) Regulations: BFSI organizations must comply with AML regulations to prevent illicit financial activities. This involves implementing systems and processes to monitor and report suspicious transactions, perform due diligence, and maintain accurate records.

Conclusion 

Data Assurance Services are crucial for maintaining trust, security, and regulatory compliance in healthcare and BFSI industries. Secure storage techniques, stringent security measures, and adherence to regulations like HIPAA, GDPR, PCI DSS, and AML foster trust and resilience. In today’s landscape of increasing data breaches and cyber threats, Data Assurance Services gain prominence as organizations secure sensitive information while meeting evolving compliance standards. Both healthcare and BFSI sectors, holding critical data, require strong strategies for data availability, confidentiality, and integrity.

These sectors embrace advanced technologies like AI and blockchain to enhance data assurance. These technologies offer improved encryption and decentralized storage, bolstering security protocols.

The interplay between data assurance and emerging technologies emphasises the need for ongoing adaptation. Beyond traditional bounds, data assurance encompasses data from IoT devices, wearables, patient monitoring, and mobile banking. Safeguarding data in transit and at network edges is as vital as protecting centralised repositories.

Tackling these challenges necessitates industry collaboration and knowledge sharing. Forums for professionals to exchange insights and strengthen defences against evolving cyber threats Government regulators and industry associations also guide robust data security through standards and enforcement.

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More than ever, the human race is discovering, revolving, and revolving. The revolution in Artificial Intelligence Domain has brought the whole human species to a new Dawn of personalized services. With more people adapting to the Internet, demands of various services in different phases of life are increasing. Let’s consider the case of Covid Pandemic, the demons are still at war with. In the times of lockdown, to stay motivated we have used Audio Book applications, video broadcasting applications, attended online exercise, Yoga, even Consulted with Doctors through an Application. While the physical streets were closed, there was more traffic online.

All these applications, websites, which we have used, have a simple goal and that is a better service to the user. To do so, they collect personal information directly or indirectly, intentionally or for the sake of betterment. The machines, despite their size starting from laptops to smart watches, even voice assistants are listening to us, watching us every move we made, every word we uttered. Albeit their purpose of doing so is noble, but there’s no guarantee of leakage-proof, intruder-proof and spammers-proof data handling. According to a study by Forbes, on average there are 2.5 quintillion bytes of data generated per day, and this data is increasing year by year exponentially. Data Mining, Data Ingestion and Migration phases are the most vulnerable phases for potential data leakage. The surprising news is the cyber-attacks also happen at a rate of 18 attacks per minute. More than 16 lakh cybercrimes happened in last 3 years in India only.

Need of Data Masking

Besides the online scams and frauds Cyber Attacks, data breaches are major risks to every organization that mines personal data. A data breach is where the attacker gains access to millions to billions of people’s personal information like bank details, mobile numbers, social service numbers, etc. According to the Identity Theft Resource Center (ITRC), 83% of the 1,862 data breaches in 2021 involved sensitive data. These incidents are now considered as equipment of modern warfare.

Data Security Standards

Based on the countries and regulatory authorities there are different rules that need to be imposed to protect sensitive information. European Union States promotes General Data Protection Regulation (GDPR) to protect personal and racial information along with digital information, Health records, biometric and genetic data of individuals. United States Department of Health and Human Service (HHS) passed Health Insurance Portability and Accountability Act that protects and promotes security standards for Privacy of Individually Identifiable Health Information. International Organization for Standardization and the International Electrotechnical Commission’s (IOS/IEC) 27001 and 27018 security standards promote confidentiality, integrity and availability norms for Big Data organizations. In Extract Transform and Load (ETL) services, Data Pipeline services or Data Analytics services sticking to these security norms are crucial and liberating.

Different Security Standards

Read this insightful blog post on– Maximizing AI and ML Performance: A Guide to Effective Data Collection, Storage, and Analysis

Techniques to Protect Sensitive Data

All the security protocols and standards can be summarized into three different techniques: Data De-Identification, Data Encoding and Data Masking. Data De-identification is used to protect sensitive data by removing or obscuring identifiable information. In De-identification technique the original sensitive information will be anonymized i.e., to completely remove those records from database, pseudonymization i.e., to replace the sensitive information with aliases, and lastly the aggregation where data will be grouped and summarized and then will be presented or shared rather than sharing the original elements.

In de-identification the original data format or structure may not be retained. Data Encoding refers to the technique of encoding the data in cyphers which can later be decoded by authorized users. Various encoding techniques are Encryption – key based encryption of data, Hashing – Original data will be converted to hash values using Message Digest (md5), Secure Hash Algorithm (sha1) or BLAKE hashing, etc. In other hand Data masking is the technique of replacing the original data with factious or obfuscated data where the masked data retains the format and structure of original data. All these techniques do not fall into a particular class or follow a hierarchal trend. They are used alone with one another based on the use cases and the cruciality of the data.

Comparative abstraction of major techniques

Data Masking is of two types i.e., Static Data Masking (SDM) and Dynamic Data Masking (DDM). Static Data masking involves replacing sensitive data with realistic but fictitious data with the structure and format of original data. Static Data Masking involves substitution – replacing the sensitive data with fake data, Shuffling – Shuffle the data in a column to manipulate original value and its references, Nulling – Sensitive data will be replaced with Null values. Encryption – encryption of sensitive information, Redaction – partially masking the sensitive data where only one part of the data is visible. Whereas Dynamic Data Masking involves Full masking, partial masking – Mask portion, random masking – mask at random, conditional masking – mask when a specific condition is met, Encoding and Tokenization- convert data to non-sensitive token value that preserves the format and length of original data.

SDM masks data at rest by creating a copy of an existing data set. The copied and masked data can only be used to share in analysis and production teams. Updates to the original data do not reflect in masked data until a new copy is made whereas DDM masks data at query time. The updated data also comes in masked format because of the query. The liveness of data remains intact without worrying about data silos. SDM is the primary choice of data practitioners as it is reliable and completely isolated original data. In other hand, DDM depends on query time masking which poses a chance of failure at some adverse instances.

SDM vs DDM

Data Masking Best Practices

Masking of sensitive data depends on the use case of the resultant masked data. It is always recommended to mask the data in the non-production environment. However, there are some practices that need to be considered for secure and fault-tolerant data masking.

1. Governance: The organization must follow common security practices based on the country it’s operating in and the international data security standards as well.

2. Referential Integrity: Tables with masked data should follow references accordingly for the purpose of join while analyzing the data without revealing sensitive information.

3. Performance and Cost: Tokenization and Hashing often convert the data to a standard size which may be more than actual size. Masked data shouldn’t impact the general query processing time.

4. Scalability: In case of big data the masking technique should be able to mask large dataset and stream data as well.

5. Fault-tolerance: The technique should be tolerant to minimal data ugliness like extra space, comma, special characters etc. By scrutinizing the masking process and resultant data often helps to avoid common pitfalls.

Conclusion

In conclusion, the advancements in technology, particularly in the domain of Artificial Intelligence, have brought about a significant change in the way humans interact with services and each other. The COVID-19 pandemic has further accelerated the adoption of digital technologies as people were forced to stay indoors and seek personalized services online. The increased demand for online services during the pandemic has shown that technology can be leveraged to improve our lives and bring us closer to one another even in times of crisis. As we continue to navigate the post-pandemic world, the revolution in technology will play a significant role in shaping our future and enabling us to live a better life.

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