Data science is a powerful tool for business operations, but it also involves handling sensitive information that may be subject to privacy laws, ethical standards, and security risks. How can data scientists protect the data they collect, analyze, and share without compromising the quality and value of their work? Here are some tips and best practices to follow.
Before you start working with any data, you need to know where it comes from, who owns it, and what rules apply to it. For example, if you are dealing with personal data, you may need to comply with the General Data Protection Regulation (GDPR) in the European Union, or the California Consumer Privacy Act (CCPA) in the US. These regulations require you to obtain consent, inform users, limit access, and delete data when requested. You also need to respect the intellectual property and confidentiality of the data providers and partners.
This is by far the most controversial and sensitive issue that faces data professionals how can we use data from various sources whilst maintaining privacy and confidentiality of data this is an incredibly delicate balance we have to strike on a daily basis as we receive spreadsheet ,customer surveys a lot of data professionals are imprisoned for inadvertently infringing upon data protection rights /even leaking data or sharing it this does represent a real dilemma for all of us even when we quit our jobs we feel terribly apprehensive about being pursued legally for preaching the data particularly for profit organizations we all remember how mark zuckerberg was drilled in front of US senate over our data privacy concerns they castigated him
To protect sensitive information in business operations, data scientists should: Identify and document all data sources. Categorize data sources based on sensitivity. Maintain an inventory of data assets and their origins. Be knowledgeable about relevant data protection regulations (e.g., GDPR, HIPAA). Ensure compliance with local and international data laws. Stay updated on evolving regulatory requirements. Implement access controls and permissions based on data sensitivity. Restrict data access to authorized personnel. Monitor and audit data access to enforce security. By thoroughly understanding data sources and regulations, data scientists can effectively protect sensitive information in business operations.
There are Many ways to protect data, some are like by implementing robust security measures, such as encryption, access controls, data anonymization, and strict data governance policies, while also ensuring compliance with relevant data privacy regulations. They should regularly audit and monitor data access, collaborate with IT and security teams, and educate themselves and colleagues on data ethics and secure coding practices. Developing an incident response plan and conducting security assessments further fortify the protection of sensitive data, all while practicing data minimization and secure data sharing to mitigate risks effectively.
Either you remove all sensitive yet unnecessary data from the dataset, like names, or you encrypt it with some anonymization techniques. I do not see many ways around that. Auditing procedures should be in place to guarantee that such sensitive data are protected at any time.
To me, understanding data sources and regulations is like navigating a map with its unique landmarks and boundaries. Just as a traveler must respect local customs and laws, I must adhere to data regulations, ensuring ethical and legal use. Like a guidebook that offers insights into a region's culture, GDPR and CCPA serve as crucial references, ensuring I treat data with the respect and privacy it deserves. Venturing into the world of data without this understanding is akin to traveling blindfolded. It's not just about harnessing data, but doing so responsibly, ensuring every step taken is informed and respectful of the data's origins and rules.
One of the most effective ways to protect sensitive information is to encrypt it, which means using a code or algorithm to transform it into an unreadable format. Encryption can be applied to data in transit, such as when you send or receive it over a network, or data at rest, such as when you store it on a disk or a cloud service. You can use various encryption methods, such as symmetric, asymmetric, or hybrid encryption, depending on your needs and resources. Another way to protect sensitive information is to anonymize it, which means removing or masking any identifiers that can link the data to a specific person or entity. Anonymization can be done by techniques such as aggregation, pseudonymization, or differential privacy.
To protect sensitive information in business operations, data scientists should employ encryption to safeguard data during transmission and storage by using robust encryption methods like SSL/TLS and AES. Additionally, they should anonymize data by applying techniques such as data masking, tokenization, and generalization to eliminate or replace personally identifiable information and sensitive data, ensuring that the data remains anonymous and secure. These measures are essential to maintain the confidentiality and integrity of sensitive information in business processes while complying with data protection regulations.
Associate Data Scientist | United Airlines | MSc. Statistics | University of Delhi
How symmetric, asymmetric and hybrid encryption are different? 1. Symmetric Encryption: Think of it like a shared key for a treasure chest, where you and your friend use the same key. It's used in ATM transactions for secure card verification. 2. Asymmetric Encryption: This is like having a public key to lock a box and a private key to unlock it. It's commonly used for secure email communication. 3. Hybrid Encryption: It's a combination of both. You send a locked box (public key) and put a message secured with a shared key inside. Online shopping often uses this method for secure credit card transactions.
Data scientists and machine learning engineers play a crucial role in the data preprocessing and processing phases. As part of their responsibilities, they thoroughly analyze and understand the data they work with. Importantly, it is their duty to safeguard sensitive customer and product data from unauthorized access. This is achieved through the implementation of robust security measures such as data encryption and data masking techniques. These safeguards ensure that sensitive information remains confidential and protected, shielding it from any potential threats or breaches.
Data scientists can protect sensitive information in business operations by: Encryption: Implement strong encryption protocols for data storage and transmission. Access Control: Restrict data access to authorized personnel through robust authentication and authorization mechanisms. Anonymization: Replace identifiable information with pseudonyms or aggregate data to prevent personal identification. Data Masking: Conceal sensitive details in reports and analytics shared with non-privileged users. Regular Auditing: Conduct regular audits to identify vulnerabilities and ensure compliance with data protection regulations.
Imagine you're sending a secret recipe via a postcard. Would you want everyone to see it? Probably not. So, you'd use a secret code only you and the recipient understand. That's encryption in the data world. Now, if you replace ingredient names with generic ones, like 'spice1' or 'veggie2', making the original hard to decipher, you're anonymizing. Just as you'd safeguard a family recipe, encryption and anonymization ensure data's essence remains, but prying eyes can't misuse it.
Another important aspect of data protection is to limit who can access and modify the data, and to keep track of what they do with it. Access control is the process of granting or denying permissions to users or groups based on their roles, responsibilities, and credentials. For example, you can use role-based access control (RBAC) to assign different levels of access to different types of data, such as read-only, write-only, or full access. Audit trails are the records of the actions and events that occur on the data, such as who accessed it, when, why, and how. Audit trails can help you monitor and verify the data usage and integrity, as well as detect and prevent unauthorized or malicious activities.
Consider a practical example of access control and audit trails in a healthcare organization. In this scenario, access control is implemented by granting healthcare providers and administrators access only to patient records related to their specific responsibilities. Regular reviews of access privileges ensure that no one gains unnecessary access. Simultaneously, audit trails record each access instance, tracking who viewed patient data and when. If an unauthorized employee attempts to access sensitive patient information or a breach occurs, these logs enable the organization to swiftly detect and address the issue, thereby safeguarding patient confidentiality and complying with healthcare data privacy regulations.
Data can take different shapes such as structured or unstructured. Generally speaking, its much easier to protect structured data while wrangling it or reshaping it. However, the challenges are dominant while working with unstructured data or NoSQL type of data such as videos, audio, verbatims etc…open source platforms undergo some data leaks thus may not be safe because of their openness to its wide area support community. Therefore, relying on commercial platforms while dealing with sensitive data is strongly recommended. Naturally, data scientists work remotely, stakeholders could develop secured data pipelines to exchange data / ML / DL models by following encrypted features and protocols. This will lead to optimal security performance.
There are two types of access controls that any organization needs to have a handle on: technical and administrative. Technical controls are implemented with systems and can include permissions settings, encryption, and user authentication. Administrative controls, on the other hand, encompass policies and procedures that define the rules themselves. Before implementing a data classification scheme and RBAC, an organization needs to clarify its user base, information domains, and regulations. Programmed restrictions are only part of the picture. It's equally important to ensure that the written policies are appropriate and well understood by your userbase and administrators.
Data Sharing Agreements: When sharing data externally, create agreements specifying data usage terms. Clearly state usage purpose and constraints, including bans on unauthorized sharing or secondary use. Describe security requirements for data protection, encompassing encryption, access controls & auditing.
Successfully democratizing access to data science products in complex and sensitive environments (like healthcare) requires fine-grained access controls. Relying on walled gardens where data scientists alone can manipulate sensitive data creates several problems: 1. Single points of failure in your data security architecture 2. All-or-nothing administrative controls that either severely bottleneck innovation or needlessly expose sensitive data 3. Data deserts within your org where leaders and operators are unable to access the insights they need Fine-grained controls like column- and field-level permissions, data masking, and sensitive data access alerts unlock data scientists' full impact to the whole org without sacrificing security
The tools and platforms you use to perform data science tasks can also affect the security and privacy of the data. You should choose tools and platforms that are reliable, up-to-date, and compliant with the relevant standards and regulations. For example, you should use tools that support encryption, anonymization, access control, and audit trails, as well as tools that have robust authentication, authorization, and logging mechanisms. You should also use platforms that offer secure storage, backup, and recovery options, as well as platforms that have adequate firewalls, antivirus, and anti-malware software.
Use secure tools like IBM Cloud Pak for Data supporting encryption, access controls, and activity logging. Watson Studio provides ML tooling harnessing these security capabilities. And IBM Cloud offers secure storage, networking, and access built to enterprise standards. We enable data science securely by leveraging IBM's secure and compliant platforms.
Using secure tools and platforms is vital for data scientists. It safeguards data, ensures legal compliance, and mitigates risks. Robust authentication, encryption, and audit trails enhance data privacy. Reliable tools improve efficiency and trust with stakeholders while protecting against data breaches and malware threats.
Pay attention to where data is stored in each of the services used. Some services, such as cloud providers, ensure your data stays in your own protected area. Other services disclaim that they use your data for model training purposes.
Even reliable, compliant, and up-to-date third party tools and platforms frequently and sporadically fail in ensuring data security. While it is important to assess their effectiveness before establishing trust, it is also just as vital (if not more) to create a contingency plan for each tool and platform in case they become compromised.
Data scientists have a range of secure tools and platforms at their disposal to protect sensitive data. These include encryption tools like OpenSSL and BitLocker, access control systems such as Active Directory, and data anonymization services like ARX. Secure cloud platforms like AWS and Azure offer robust security features, while data masking solutions like Delphix and DLP tools such as Symantec help prevent data leakage. Endpoint protection tools, secure communication platforms, and data governance solutions further enhance data security, ensuring compliance with relevant standards.
Finally, one of the most essential steps to protect sensitive information is to educate yourself and others about the importance and the best practices of data security and privacy. You should stay informed and updated on the latest trends, threats, and solutions in the field of data protection, and follow the guidelines and policies of your organization and industry. You should also communicate and collaborate with other data scientists, data providers, and data users, and share your knowledge and experience with them. By doing so, you can foster a culture of data protection that benefits everyone involved.
Educating our data scientists and AI Engineers on security and privacy best practices is crucial, especially as we deliver projects for our IBM customers. We put in place mandatory training to make sure we implement innovative and ethical solutions with the right process for handling sensitive data. We focus on fostering a culture of earning trust through transparency, accountability, and responsible data stewardship with our clients.
Data literacy plays a crucial role in safeguarding sensitive information. It is essential for companies to conduct dedicated data literacy sessions to educate newcomers about data policies and the consequences of data breaches. Additionally, Data Scientists and Analysts should only utilize sensitive attributes when absolutely necessary for their specific use cases.
Soon, there will be many new rules and regulations worldwide to protect user data. As a data scientist, it's vital to keep up with these changes. Additionally, having regular discussions within the organization about data privacy is important. It helps data scientists make smart choices when dealing with sensitive data, ensuring it's handled responsibly and securely.
Data scientists can educate the general audience about data security by simplifying complex concepts, offering practical tips, and raising awareness about the risks. They should emphasize the importance of secure practices, like using strong passwords, keeping software up-to-date, and recognizing phishing attempts. Demonstrating real-world consequences of data breaches and providing user-friendly resources can motivate individuals to take data security seriously. Furthermore, promoting a culture of vigilance and ethical responsibility, both at work and in personal life, fosters a strong commitment to safeguarding sensitive data vital for business operations.
Continuous education is essential for data scientists and their teams. Stay updated on the latest security best practices, tools, and techniques. Educate colleagues and team members about data security, privacy, and the importance of compliance. Regular training can help create a culture of data security within the organization.
Balancing data privacy with model performance is a tricky task. When there's not enough labeled data, especially with sensitive information, it can make it hard to train the algorithm well. Even if we use techniques to hide identities, there's still a risk of someone figuring it out. Also, keeping the data secure while building the model is challenging. It needs strong encryption and access controls. To handle these problems, we have to take a comprehensive approach that includes following the law, thinking about ethics, and keeping a close eye on things to stop data breaches and biases in the algorithm. This helps us keep both privacy and the model's effectiveness intact.
Another approach is to consider what minimal amount of data is required to get a business result, drive an insight, or provide analytics. In majority of cases, fine-grained PII, sensitive absolute numbers, raw-level data about customers is not really needed and is only used because it is easily available. After one takes a critical look at what data is useful (and it is usually a small fraction of what is collected), one should look into rules and regulations, compliance and ethical guidelines, etc. Usually, data can be anonymized, obfuscated, aggregated in a way that protects privacy, follows the regulation, and still delivers value. Ideally, those practices should be built into the data pipelines and audited by trusted third parties.
From an IP practitioner perspective, I would also advocate implementation of measures to identify, record and protect trade secrets such as sensitive data and algorithms. Trade secret theft is a criminal offence in most US states and in Canada. Legally an organization is required to show reasonable effort in maintaining secrecy. Having a solid IP policy which incorporates trade secret measures would be a good first step.
CISO (Chief Information and Security Officer) function is an important role in organisations nowadays. And CTOs' can take up this role to ensure data and information literacy across the organisation. And an example of educating the organizations in the Indian context is training not just Data and AI Professionals, but everyone in the organisation including engineering and non technical roles on ISO ISMS Trainings. The ISO ISMS Trainings and evaluation should be mandatory for all the new employees and yearly revision for tenured employees. This helps everyone develop a data-protection approach in the situation of handling sensitive data.
Chief Learn-It-All | AI/ML Master's Degree, Driving Innovation
We all know how to secure the raw data on which our AI/ML models are trained. What we often forget is that our trained models captured data that can reveal sensitive information about our business operations. We must also strive to keep the trained models, protected. It's become more important these days, with chat apps based on LLMs.