برچسب: Data

  • Why Data Principal Rights Management Is the Heart of Modern Privacy Compliance|Seqrite

    Why Data Principal Rights Management Is the Heart of Modern Privacy Compliance|Seqrite


    As data privacy laws evolve globally—from the GDPR to India’s Digital Personal Data Protection Act (DPDPA)—one common theme emerges: empowering individuals with control over their data. This shift places data principal rights at the center of privacy compliance.

    Respecting these rights isn’t just a legal obligation for organizations; it’s a business imperative. Efficiently operationalizing and fulfilling data principal rights is now a cornerstone of modern privacy programs.

    Understanding Data Principal Rights

    Data principal rights refer to the entitlements granted to individuals regarding their data. Under laws like the DPDPA and GDPR, these typically include:

    • Right to Access: Individuals can request a copy of the personal data held about them.
    • Right to Correction: They can demand corrections to inaccurate or outdated data.
    • Right to Erasure (Right to Be Forgotten): They can request deletion of their data under specific circumstances.
    • Right to Data Portability: They can request their data in a machine-readable format.
    • Right to Withdraw Consent: They can withdraw previously given consent for data processing.
    • Right to Grievance Redressal: They can lodge complaints if their rights are not respected.

    While these rights sound straightforward, fulfilling them at scale is anything but simple, especially when data is scattered across cloud platforms, internal systems, and third-party applications.

    Why Data Principal Rights Management is Critical

    1. Regulatory Compliance and Avoidance of Penalties

    Non-compliance can result in substantial fines, regulatory scrutiny, and reputational harm. For instance, DPDPA empowers the Data Protection Board of India to impose heavy penalties for failure to honor data principal rights on time.

    1. Customer Trust and Transparency

    Respecting user rights builds transparency and demonstrates that your organization values privacy. This can increase customer loyalty and strengthen brand reputation in privacy-conscious markets.

    1. Operational Readiness and Risk Reduction

    Organizations risk delays, errors, and missed deadlines when rights requests are handled manually. An automated and structured rights management process reduces legal risk and improves operational agility.

    1. Auditability and Accountability

    Every action taken to fulfill a rights request must be logged and documented. This is essential for proving compliance during audits or investigations.

    The Role of Data Discovery in Rights Fulfilment

    To respond to any data principal request, you must first know where the relevant personal data resides. This is where Data Discovery plays a crucial supporting role.

    A robust data discovery framework enables organizations to:

    • Identify all systems and repositories that store personal data.
    • Correlate data to specific individuals or identifiers.
    • Retrieve, correct, delete, or port data accurately and quickly.

    Without comprehensive data visibility, any data principal rights management program will fail, resulting in delays, partial responses, or non-compliance.

    Key Challenges in Rights Management

    Despite its importance, many organizations struggle with implementing effective data principal rights management due to:

    • Fragmented data environments: Personal data is often stored in silos, making it challenging to aggregate and act upon.
    • Manual workflows: Fulfilling rights requests often involves slow, error-prone manual processes.
    • Authentication complexities: Verifying the identity of the data principal securely is essential to prevent abuse of rights.
    • Lack of audit trails: Without automated tracking, it’s hard to demonstrate compliance.

    Building a Scalable Data Principal Rights Management Framework

    To overcome these challenges, organizations must invest in technologies and workflows that automate and streamline the lifecycle of rights requests. A mature data principal rights management framework should include:

    • Centralized request intake: A portal or dashboard where individuals can easily submit rights requests.
    • Automated data mapping: Leveraging data discovery tools to locate relevant personal data quickly.
    • Workflow automation: Routing requests to appropriate teams with built-in deadlines and escalation paths.
    • Verification and consent tracking: Only verified individuals can initiate requests and track their consent history.
    • Comprehensive logging: Maintaining a tamper-proof audit trail of all actions to fulfill requests.

    The Future of Privacy Lies in Empowerment

    As data privacy regulations mature, the focus shifts from mere protection to empowerment. Data principles are no longer passive subjects but active stakeholders in handling their data. Organizations that embed data principal rights management into their core data governance strategy will stay compliant and gain a competitive edge in building customer trust.

    Empower Your Privacy Program with Seqrite

    Seqrite’s Data Privacy Suite is purpose-built to help enterprises manage data principal rights confidently. From automated request intake and identity verification to real-time data discovery and audit-ready logs, Seqrite empowers you to comply faster, smarter, and at scale.



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  • C# Tip: ObservableCollection – a data type to intercept changes to the collection | Code4IT

    C# Tip: ObservableCollection – a data type to intercept changes to the collection | Code4IT


    Just a second! 🫷
    If you are here, it means that you are a software developer.
    So, you know that storage, networking, and domain management have a cost .

    If you want to support this blog, please ensure that you have disabled the adblocker for this site.
    I configured Google AdSense to show as few ADS as possible – I don’t want to bother you with lots of ads, but I still need to add some to pay for the resources for my site.

    Thank you for your understanding.
    Davide

    Imagine you need a way to raise events whenever an item is added or removed from a collection.

    Instead of building a new class from scratch, you can use ObservableCollection<T> to store items, raise events, and act when the internal state of the collection changes.

    In this article, we will learn how to use ObservableCollection<T>, an out-of-the-box collection available in .NET.

    Introducing the ObservableCollection type

    ObservableCollection<T> is a generic collection coming from the System.Collections.ObjectModel namespace.

    It allows the most common operations, such as Add<T>(T item) and Remove<T>(T item), as you can expect from most of the collections in .NET.

    Moreover, it implements two interfaces:

    • INotifyCollectionChanged can be used to raise events when the internal collection is changed.
    • INotifyPropertyChanged can be used to raise events when one of the properties of the changes.

    Let’s see a simple example of the usage:

    var collection = new ObservableCollection<string>();
    
    collection.Add("Mario");
    collection.Add("Luigi");
    collection.Add("Peach");
    collection.Add("Bowser");
    
    collection.Remove("Luigi");
    
    collection.Add("Waluigi");
    
    _ = collection.Contains("Peach");
    
    collection.Move(1, 2);
    

    As you can see, we can do all the basic operations: add, remove, swap items (with the Move method), and check if the collection contains a specific value.

    You can simplify the initialization by passing a collection in the constructor:

     var collection = new ObservableCollection<string>(new string[] { "Mario", "Luigi", "Peach" });
    
     collection.Add("Bowser");
    
     collection.Remove("Luigi");
    
     collection.Add("Waluigi");
    
     _ = collection.Contains("Peach");
    
     collection.Move(1, 2);
    

    How to intercept changes to the underlying collection

    As we said, this data type implements INotifyCollectionChanged. Thanks to this interface, we can add event handlers to the CollectionChanged event and see what happens.

    var collection = new ObservableCollection<string>(new string[] { "Mario", "Luigi", "Peach" });
    collection.CollectionChanged += WhenCollectionChanges;
    
    Console.WriteLine("Adding Bowser...");
    collection.Add("Bowser");
    Console.WriteLine("");
    
    
    Console.WriteLine("Removing Luigi...");
    collection.Remove("Luigi");
    Console.WriteLine("");
    
    Console.WriteLine("Adding Waluigi...");
    collection.Add("Waluigi");
    Console.WriteLine("");
    
    Console.WriteLine("Searching for Peach...");
    var containsPeach = collection.Contains("Peach");
    Console.WriteLine("");
    
    Console.WriteLine("Swapping items...");
    collection.Move(1, 2);
    

    The WhenCollectionChanges method accepts a NotifyCollectionChangedEventArgs that gives you info about the intercepted changes:

    private void WhenCollectionChanges(object? sender, NotifyCollectionChangedEventArgs e)
    {
        var allItems = ((IEnumerable<object>)sender)?.Cast<string>().ToArray() ?? new string[] { "<empty>" };
        Console.WriteLine($"> Currently, the collection is {string.Join(',', allItems)}");
    
        Console.WriteLine($"> The operation is {e.Action}");
    
        var previousItems = e.OldItems?.Cast<string>()?.ToArray() ?? new string[] { "<empty>" };
        Console.WriteLine($"> Before the operation it was {string.Join(',', previousItems)}");
    
    
        var currentItems = e.NewItems?.Cast<string>()?.ToArray() ?? new string[] { "<empty>" };
        Console.WriteLine($"> Now, it is {string.Join(',', currentItems)}");
    }
    

    Every time an operation occurs, we write some logs.

    The result is:

    Adding Bowser...
    > Currently, the collection is Mario,Luigi,Peach,Bowser
    > The operation is Add
    > Before the operation it was <empty>
    > Now, it is Bowser
    
    Removing Luigi...
    > Currently, the collection is Mario,Peach,Bowser
    > The operation is Remove
    > Before the operation it was Luigi
    > Now, it is <empty>
    
    Adding Waluigi...
    > Currently, the collection is Mario,Peach,Bowser,Waluigi
    > The operation is Add
    > Before the operation it was <empty>
    > Now, it is Waluigi
    
    Searching for Peach...
    
    Swapping items...
    > Currently, the collection is Mario,Bowser,Peach,Waluigi
    > The operation is Move
    > Before the operation it was Peach
    > Now, it is Peach
    

    Notice a few points:

    • the sender property holds the current items in the collection. It’s an object?, so you have to cast it to another type to use it.
    • the NotifyCollectionChangedEventArgs has different meanings depending on the operation:
      • when adding a value, OldItems is null and NewItems contains the items added during the operation;
      • when removing an item, OldItems contains the value just removed, and NewItems is null.
      • when swapping two items, both OldItems and NewItems contain the item you are moving.

    How to intercept when a collection property has changed

    To execute events when a property changes, we need to add a delegate to the PropertyChanged event. However, it’s not available directly on the ObservableCollection type: you first have to cast it to an INotifyPropertyChanged:

    var collection = new ObservableCollection<string>(new string[] { "Mario", "Luigi", "Peach" });
    (collection as INotifyPropertyChanged).PropertyChanged += WhenPropertyChanges;
    
    Console.WriteLine("Adding Bowser...");
    collection.Add("Bowser");
    Console.WriteLine("");
    
    
    Console.WriteLine("Removing Luigi...");
    collection.Remove("Luigi");
    Console.WriteLine("");
    
    Console.WriteLine("Adding Waluigi...");
    collection.Add("Waluigi");
    Console.WriteLine("");
    
    Console.WriteLine("Searching for Peach...");
    var containsPeach = collection.Contains("Peach");
    Console.WriteLine("");
    
    Console.WriteLine("Swapping items...");
    collection.Move(1, 2);
    

    We can now specify the WhenPropertyChanges method as such:

    private void WhenPropertyChanges(object? sender, PropertyChangedEventArgs e)
    {
        var allItems = ((IEnumerable<object>)sender)?.Cast<string>().ToArray() ?? new string[] { "<empty>" };
        Console.WriteLine($"> Currently, the collection is {string.Join(',', allItems)}");
        Console.WriteLine($"> Property {e.PropertyName} has changed");
    }
    

    As you can see, we have again the sender parameter that contains the collection of items.

    Then, we have a parameter of type PropertyChangedEventArgs that we can use to get the name of the property that has changed, using the PropertyName property.

    Let’s run it.

    Adding Bowser...
    > Currently, the collection is Mario,Luigi,Peach,Bowser
    > Property Count has changed
    > Currently, the collection is Mario,Luigi,Peach,Bowser
    > Property Item[] has changed
    
    Removing Luigi...
    > Currently, the collection is Mario,Peach,Bowser
    > Property Count has changed
    > Currently, the collection is Mario,Peach,Bowser
    > Property Item[] has changed
    
    Adding Waluigi...
    > Currently, the collection is Mario,Peach,Bowser,Waluigi
    > Property Count has changed
    > Currently, the collection is Mario,Peach,Bowser,Waluigi
    > Property Item[] has changed
    
    Searching for Peach...
    
    Swapping items...
    > Currently, the collection is Mario,Bowser,Peach,Waluigi
    > Property Item[] has changed
    

    As you can see, for every add/remove operation, we have two events raised: one to say that the Count has changed, and one to say that the internal Item[] is changed.

    However, notice what happens in the Swapping section: since you just change the order of the items, the Count property does not change.

    This article first appeared on Code4IT 🐧

    Final words

    As you probably noticed, events are fired after the collection has been initialized. Clearly, it considers the items passed in the constructor as the initial state, and all the subsequent operations that mutate the state can raise events.

    Also, notice that events are fired only if the reference to the value changes. If the collection holds more complex classes, like:

    public class User
    {
        public string Name { get; set; }
    }
    

    No event is fired if you change the value of the Name property of an object already part of the collection:

    var me = new User { Name = "Davide" };
    var collection = new ObservableCollection<User>(new User[] { me });
    
    collection.CollectionChanged += WhenCollectionChanges;
    (collection as INotifyPropertyChanged).PropertyChanged += WhenPropertyChanges;
    
    me.Name = "Updated"; // It does not fire any event!
    

    Notice that ObservableCollection<T> is not thread-safe! You can find an interesting article by Gérald Barré (aka Meziantou) where he explains a thread-safe version of ObservableCollection<T> he created. Check it out!

    As always, I suggest exploring the language and toying with the parameters, properties, data types, etc.

    You’ll find lots of exciting things that may come in handy.

    I hope you enjoyed this article! Let’s keep in touch on Twitter or LinkedIn! 🤜🤛

    Happy coding!

    🐧





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  • Seeding in-memory Entity Framework with realistic data with Bogus &vert; Code4IT

    Seeding in-memory Entity Framework with realistic data with Bogus | Code4IT


    You don’t need a physical database to experiment with ORMs. You can use an in-memory DB and seed the database with realistic data generated with Bogus.

    Table of Contents

    Just a second! 🫷
    If you are here, it means that you are a software developer.
    So, you know that storage, networking, and domain management have a cost .

    If you want to support this blog, please ensure that you have disabled the adblocker for this site.
    I configured Google AdSense to show as few ADS as possible – I don’t want to bother you with lots of ads, but I still need to add some to pay for the resources for my site.

    Thank you for your understanding.
    Davide

    Sometimes, you want to experiment with some features or create a demo project, but you don’t want to instantiate a real database instance.

    Also, you might want to use some realistic data – not just “test1”, 123, and so on. These values are easy to set but not very practical when demonstrating functionalities.

    In this article, we’re going to solve this problem by using Bogus and Entity Framework: you will learn how to generate realistic data and how to store them in an in-memory database.

    Bogus, a C# library for generating realistic data

    Bogus is a popular library for generating realistic data for your tests. It allows you to choose the category of dummy data that best suits your needs.

    It all starts by installing Bogus via NuGet by running Install-Package Bogus.

    From here, you can define the so-called Fakers, whose purpose is to generate dummy instances of your classes by auto-populating their fields.

    Let’s see a simple example. We have this POCO class named Book:

    public class Book
    {
        public Guid Id { get; set; }
        public string Title { get; set; }
        public int PagesCount { get; set; }
        public Genre[] Genres { get; set; }
        public DateOnly PublicationDate { get; set; }
        public string AuthorFirstName { get; set; }
        public string AuthorLastName { get; set; }
    }
    
    public enum Genre
    {
        Thriller, Fantasy, Romance, Biography
    }
    

    Note: for the sake of simplicity, I used a dumb approach: author’s first and last name are part of the Book info itself, and the Genres property is treated as an array of enums and not as a flagged enum.

    From here, we can start creating our Faker by specifying the referenced type:

    Faker<Book> bookFaker = new Faker<Book>();
    

    We can add one or more RuleFor methods to create rules used to generate each property.

    The simplest approach is to use the overload where the first parameter is a Function pointing to the property to be populated, and the second is a Function that calls the methods provided by Bogus to create dummy data.

    Think of it as this pseudocode:

    faker.RuleFor(sm => sm.SomeProperty, f => f.SomeKindOfGenerator.GenerateSomething());
    

    Another approach is to pass as the first argument the name of the property like this:

    faker.RuleFor("myName", f=> f.SomeKindOfGenerator.GenerateSomething())
    

    A third approach is to define a generator for a specific type, saying “every time you’re trying to map a property with this type, use this generator”:

    bookFaker.RuleForType(typeof(DateOnly), f => f.Date.PastDateOnly());
    

    Let’s dive deeper into Bogus, generating data for common types.

    Generate random IDs with Bogus

    We can generate random GUIDs like this:

    bookFaker.RuleFor(b => b.Id, f => f.Random.Guid());
    

    In a similar way, you can generate Uuid by calling f.Random.Uuid().

    Generate random text with Bogus

    We can generate random text, following the Lorem Ipsum structure, to pick a single word or a longer text:

    Using Text you generate random text:

    bookFaker.RuleFor(b => b.Title, f => f.Lorem.Text());
    

    However, you can use several other methods to generate text with different lengths, such as Letter, Word, Paragraphs, Sentences, and more.

    Working with Enums with Bogus

    If you have an enum, you can rely again on the Random property of the Faker and get a random subset of the enums like this:

    bookFaker.RuleFor(b => b.Genres, f => f.Random.EnumValues<Genre>(2));
    

    As you can see, I specified the number of random items to use (in this case, 2). If you don’t set it, it will take a random number of items.

    However, the previous method returns an array of elements. If you want to get a single enum, you should use f.Random.Enum<Genre>().

    One of the most exciting features of Bogus is the ability to generate realistic data for common entities, such as a person.

    In particular, you can use the Person property to generate data related to the first name, last name, Gender, UserName, Phone, Website, and much more.

    You can use it this way:

    bookFaker.RuleFor(b => b.AuthorFirstName, f => f.Person.FirstName);
    bookFaker.RuleFor(b => b.AuthorLastName, f => f.Person.LastName);
    

    Generate final class instances with Bogus

    We can generate the actual items now that we’ve defined our rules.

    You just need to call the Generate method; you can also specify the number of items to generate by passing a number as a first parameter:

    List<Book> books = bookFaker.Generate(2);
    

    Suppose you want to generate a random quantity of items. In that case, you can use the GenerateBetween method, specifying the top and bottom limit:

    List<Book> books = bookFaker.GenerateBetween(2, 5);
    

    Wrapping up the Faker example

    Now that we’ve learned how to generate a Faker, we can refactor the code to make it easier to read:

    private List<Book> GenerateBooks(int count)
    {
        Faker<Book> bookFaker = new Faker<Book>()
            .RuleFor(b => b.Id, f => f.Random.Guid())
            .RuleFor(b => b.Title, f => f.Lorem.Text())
            .RuleFor(b => b.Genres, f => f.Random.EnumValues<Genre>())
            .RuleFor(b => b.AuthorFirstName, f => f.Person.FirstName)
            .RuleFor(b => b.AuthorLastName, f => f.Person.LastName)
            .RuleFor(nameof(Book.PagesCount), f => f.Random.Number(100, 800))
            .RuleForType(typeof(DateOnly), f => f.Date.PastDateOnly());
    
        return bookFaker.Generate(count);
    }
    

    If we run it, we can see it generates the following items:

    Bogus-generated data

    Seeding InMemory Entity Framework with dummy data

    Entity Framework is among the most famous ORMs in the .NET ecosystem. Even though it supports many integrations, sometimes you just want to store your items in memory without relying on any specific database implementation.

    Using Entity Framework InMemory provider

    To add this in-memory provider, you must install the Microsoft.EntityFrameworkCore.InMemory NuGet Package.

    Now you can add a new DbContext – which is a sort of container of all the types you store in your database – ensuring that the class inherits from DbContext.

    public class BooksDbContext : DbContext
    {
        public DbSet<Book> Books { get; set; }
    }
    

    You then have to declare the type of database you want to use by defining it the int OnConfiguring method:

    protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
    {
        optionsBuilder.UseInMemoryDatabase("BooksDatabase");
    }
    

    Note: even though it’s an in-memory database, you still need to declare the database name.

    Seeding the database with data generated with Bogus

    You can seed the database using the data generated by Bogus by overriding the OnModelCreating method:

    protected override void OnModelCreating(ModelBuilder modelBuilder)
    {
        base.OnModelCreating(modelBuilder);
    
        var booksFromBogus = BogusBookGenerator.GenerateBooks(15);
    
        modelBuilder.Entity<Book>().HasData(booksFromBogus);
    }
    

    Notice that we first create the items and then, using modelBuilder.Entity<Book>().HasData(booksFromBogus), we set the newly generated items as content for the Books DbSet.

    Consume dummy data generated with EF Core

    To wrap up, here’s the complete implementation of the DbContext:

    public class BooksDbContext : DbContext
    {
        public DbSet<Book> Books { get; set; }
    
        protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
        {
          optionsBuilder.UseInMemoryDatabase("BooksDatabase");
        }
    
        protected override void OnModelCreating(ModelBuilder modelBuilder)
        {
            base.OnModelCreating(modelBuilder);
    
            var booksFromBogus = BogusBookGenerator.GenerateBooks(15);
    
            modelBuilder.Entity<Book>().HasData(booksFromBogus);
        }
    }
    

    We are now ready to instantiate the DbContext, ensure that the Database has been created and seeded with the correct data, and perform the operations needed.

    using var dbContext = new BooksDbContext();
    dbContext.Database.EnsureCreated();
    
    var allBooks = await dbContext.Books.ToListAsync();
    
    var thrillerBooks = dbContext.Books
            .Where(b => b.Genres.Contains(Genre.Thriller))
            .ToList();
    

    Further readings

    In this blog, we’ve already discussed the Entity Framework. In particular, we used it to perform CRUD operations on a PostgreSQL database.

    🔗 How to perform CRUD operations with Entity Framework Core and PostgreSQL | Code4IT

    This article first appeared on Code4IT 🐧

    I suggest you explore the potentialities of Bogus: there are a lot of functionalities that I didn’t cover in this article, and they may make your tests and experiments meaningful and easier to understand.

    🔗 Bogus repository | GitHub

    Wrapping up

    Bogus is a great library for creating unit and integration tests. However, I find it useful to generate dummy data for several purposes, like creating a stub of a service, populating a UI with realistic data, or trying out other tools and functionalities.

    I hope you enjoyed this article! Let’s keep in touch on Twitter or LinkedIn! 🤜🤛

    Happy coding!

    🐧





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  • Data Discovery and Classification for Modern Enterprises

    Data Discovery and Classification for Modern Enterprises


    In today’s high-stakes digital arena, data is the lifeblood of every enterprise. From driving strategy to unlocking customer insights, enterprises depend on data like never before. But with significant volume comes great vulnerability.

    Imagine managing a massive warehouse without labels, shelves, or a map. That’s how most organizations handle their data today—scattered across endpoints, servers, SaaS apps, and cloud platforms, much of it unidentified and unsecured. This dark, unclassified data is inefficient and dangerous.

    At Seqrite, the path to resilient data privacy and governance begins with two foundational steps: Data Discovery and Classification.

    Shedding Light on Dark Data: The Discovery Imperative

    Before protecting your data, you need to know what you have and where it resides. That’s the core of data discovery—scanning your digital landscape to locate and identify every piece of information, from structured records in databases to unstructured files in cloud folders.

    Modern Privacy tools leverage AI and pattern recognition to unearth sensitive data, whether it’s PII, financial records, or health information, often hidden in unexpected places. Shockingly, nearly 75% of enterprise data remains unused, mainly because it goes undiscovered.

    Without this visibility, every security policy and compliance program stands on shaky ground.

    Data Classification: Assigning Value and Implementing Control

    Discovery tells you what data you have. Classification tells you how to treat it.

    Is it public? Internal? Confidential? Restricted? Classification assigns your data a business context and risk level so you can apply the right protection, retention, and sharing rules.

    This is especially critical in industries governed by privacy laws like GDPR, DPDP Act, and HIPAA, where treating all data the same is both inefficient and non-compliant.

    With classification in place, you can:

    • Prioritize protection for sensitive data
    • Automate DLP and encryption policies
    • Streamline responses to individual rights requests
    • Reduce the clutter of ROT (redundant, obsolete, trivial) data

    The Power of Discovery + Classification

    Together, discovery and classification form the bedrock of data governance. Think of them as a radar system and rulebook:

    • Discovery shows you the terrain.
    • Classification helps you navigate it safely.

    When integrated into broader data security workflows – like Zero Trust access control, insider threat detection, and consent management – they multiply the impact of every security investment.

    Five Reasons Enterprises Can’t Ignore this Duo

    1. Targeted Security Where It Matters Most

    You can’t secure what you can’t see. With clarity on your sensitive data’s location and classification, you can apply fine-tuned protections such as encryption, role-based access, and DLP—only where needed. That reduces attack surfaces and simplifies security operations.

    1. Compliance Without Chaos

    Global data laws are demanding and constantly evolving. Discovery and classification help you prove accountability, map personal data flows, and respond to rights requests accurately and on time.

    1. Storage & Cost Optimization

    Storing ROT data is expensive and risky. Discovery helps you declutter, archive, or delete non-critical data while lowering infrastructure costs and improving data agility.

    1. Proactive Risk Management

    The longer a breach goes undetected, the more damage it does. By continuously discovering and classifying data, you spot anomalies and vulnerabilities early; well before they spiral into crises.

    1. Better Decisions with Trustworthy Data

    Only clean, well-classified data can fuel accurate analytics and AI. Whether it’s refining customer journeys or optimizing supply chains, data quality starts with discovery and classification.

    In Conclusion, Know your Data, Secure your Future

    In a world where data is constantly growing, moving, and evolving, the ability to discover and classify is a strategic necessity. These foundational capabilities empower organizations to go beyond reactive compliance and security, helping them build proactive, resilient, and intelligent data ecosystems.

    Whether your goal is to stay ahead of regulatory demands, reduce operational risks, or unlock smarter insights, it all starts with knowing your data. Discovery and classification don’t just minimize exposure; they create clarity, control, and confidence.

    Enterprises looking to take control of their data can rely on Seqrite’s Data Privacy solution, which delivers powerful discovery and classification capabilities to turn information into an advantage.



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  • Guide for Businesses Navigating Global Data Privacy

    Guide for Businesses Navigating Global Data Privacy


    Organizations manage personal data across multiple jurisdictions in today’s interconnected digital economy, requiring a clear understanding of global data protection frameworks. The European Union’s General Data Protection Regulation (GDPR) and India’s Digital Personal Data Protection Act (DPDP) 2023 are two key regulations shaping the data privacy landscape. This guide provides a comparative analysis of these regulations, outlining key distinctions for businesses operating across both regions.

    Understanding the GDPR: Key Considerations for Businesses

    The GDPR, enforced in May 2018, is a comprehensive data protection law that applies to any organization processing personal data of EU residents, regardless of location.

    • Territorial Scope: GDPR applies to organizations with an establishment in the EU or those that offer goods or services to, or monitor the behavior of, EU residents, requiring many global enterprises to comply.
    • Definition of Personal Data: The GDPR defines personal data as any information related to an identifiable individual. It further classifies sensitive personal data and imposes stricter processing requirements.
    • Principles of Processing: Compliance requires adherence to lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity, confidentiality, and accountability in data processing activities.
    • Lawful Basis for Processing: Businesses must establish a lawful basis for processing, such as consent, contract, legal obligation, vital interests, public task, or legitimate interest.
    • Data Subject Rights: GDPR grants individuals rights, including access, rectification, erasure, restriction, data portability, and objection to processing, necessitating dedicated mechanisms to address these requests.
    • Obligations of Controllers and Processors: GDPR imposes direct responsibilities on data controllers and processors, requiring them to implement security measures, maintain processing records, and adhere to breach notification protocols.

     

    Understanding the DPDP Act 2023: Implications for Businesses in India

    The DPDP Act 2023, enacted in August 2023, establishes a legal framework for the processing of digital personal data in India.

    • Territorial Scope: The Act applies to digital personal data processing in India and processing outside India if it involves offering goods or services to Indian data principals.
    • Definition of Personal Data: Personal data refers to any data that identifies an individual, specifically in digital form. Unlike GDPR, the Act does not differentiate between general and sensitive personal data (though future classifications may emerge).
    • Principles of Data Processing: The Act mandates lawful and transparent processing, purpose limitation, data minimization, accuracy, storage limitation, security safeguards, and accountability.
    • Lawful Basis for Processing: The primary basis for processing is explicit, informed, unconditional, and unambiguous consent, with certain legitimate exceptions.
    • Rights of Data Principals: Individuals can access, correct, and erase their data, seek grievance redressal, and nominate another person to exercise their rights if they become incapacitated.
    • Obligations of Data Fiduciaries and Processors: The Act imposes direct responsibilities on Data Fiduciaries (equivalent to GDPR controllers) to obtain consent, ensure data accuracy, implement safeguards, and report breaches. Data Processors (like GDPR processors) operate under contractual obligations set by Data Fiduciaries.

    GDPR vs. DPDP: Key Differences for Businesses 

    Feature GDPR DPDP Act 2023 Business Implications
    Data Scope Covers both digital and non-digital personal data within a filing system. Applies primarily to digital personal data. Businesses need to assess their data inventory and processing activities, particularly for non-digital data handled in India.
    Sensitive Data Explicitly defines and provides stricter rules for processing sensitive personal data. Applies a uniform standard to all digital personal data currently. Organizations should be mindful of potential future classifications of sensitive data under DPDP.
    Lawful Basis Offers multiple lawful bases for processing, including legitimate interests and contractual necessity. Primarily consent-based, with limited exceptions for legitimate uses. Businesses need to prioritize obtaining explicit consent for data processing in India and carefully evaluate the scope of legitimate use exceptions.
    Individual Rights Provides a broader range of rights, including data portability and the right to object to profiling. Focuses on core rights like access, correction, and erasure. Compliance programs should address the specific set of rights granted under the DPDP Act.
    Data Transfer Strict mechanisms for international data transfers, requiring adequacy decisions or safeguards. Permits cross-border transfers except to countries specifically restricted by the Indian government. Businesses need to monitor the list of restricted countries for data transfers from India.
    Breach Notification Requires notification to the supervisory authority if the breach is likely to result in a high risk to individuals. Mandates notification to both the Data Protection Board and affected Data Principals for all breaches. Organizations must establish comprehensive data breach response plans aligned with DPDP’s broader notification requirements.
    Enforcement Enforced by Data Protection Authorities in each EU member state. Enforced by the central Data Protection Board of India. Businesses need to be aware of the centralized enforcement mechanism under the DPDP Act.
    Data Protection Officer (DPO) Mandatory for certain organizations based on processing activities. Mandatory for Significant Data Fiduciaries, with criteria to be specified. Organizations that meet the criteria for Significant Data Fiduciaries under DPDP will need to appoint a DPO.
    Data Processor Obligations Imposes direct obligations on data processors. Obligations are primarily contractual between Data Fiduciaries and Data Processors. Data Fiduciaries in India bear greater responsibility for ensuring the compliance of their Data Processors.

     

    Navigating Global Compliance: A Strategic Approach for Businesses

    Organizations subject to GDPR and DPDP must implement a harmonized yet region-specific compliance strategy. Key focus areas include:

    • Data Mapping and Inventory: Identify and categorize personal data flows across jurisdictions to determine applicable regulatory requirements.
    • Consent Management: Implement mechanisms that align with GDPR’s “freely given, specific, informed, and unambiguous” consent standard and DPDP’s stricter “free, specific, informed, unconditional, and unambiguous” requirement. Ensure easy withdrawal options.
    • Data Security Measures: Deploy technical and organizational safeguards proportionate to data processing risks, meeting the security mandates of both regulations.
    • Data Breach Response Plan: Establish incident response protocols that meet GDPR and DPDP notification requirements, particularly DPDP’s broader scope.
    • Data Subject/Principal Rights Management: Develop workflows to handle data access, correction, and erasure requests under both regulations, ensuring compliance with response timelines.
    • Cross-Border Data Transfer Mechanisms: Implement safeguards for international data transfers, aligning with GDPR’s standard contractual clauses and DPDP’s yet-to-be-defined jurisdictional rules.
    • Appointment of DPO/Contact Person: Assess whether a Data Protection Officer (DPO) is required under GDPR or if the organization qualifies as a Significant Data Fiduciary under DPDP, necessitating a DPO or designated contact person.
    • Employee Training: Conduct training programs on data privacy laws and best practices to maintain team compliance awareness.
    • Regular Audits: Perform periodic audits to evaluate data protection measures, adapting to evolving regulatory guidelines.

    Conclusion: Towards a Global Privacy-Centric Approach

    While GDPR and the DPDP Act 2023 share a common goal of enhancing data protection, they differ in scope, consent requirements, and enforcement mechanisms. Businesses operating across multiple jurisdictions must adopt a comprehensive, adaptable compliance strategy that aligns with both regulations.

    By strengthening data governance, implementing robust security controls, and fostering a privacy-first culture, organizations can navigate global data protection challenges effectively and build trust with stakeholders.

    Seqrite offers cybersecurity and data protection solutions to help businesses achieve and maintain compliance with evolving global privacy regulations.

     



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  • Python – Data Wrangling with Excel and Pandas – Useful code

    Python – Data Wrangling with Excel and Pandas – Useful code


    Data wrangling with Excel and Pandas is actually quite useful tool in the belt of any Excel professional, financial professional, data analyst or a developer. Really, everyonecan benefit from the well defined libraries that ease people’s lifes. These are the libraries used:

    Additionally, a function for making a unique Excel name is used:

    An example of the video, where Jupyter Notebook is used.

    In the YT video below, the following 8 points are discussed:

    # Trick 1 – Simple reading of worksheet from Excel workbook

    # Trick 2 – Combine Reports

    # Trick 3 – Fix Missing Values

    # Trick 4 – Formatting the exported Excel file

    # Trick 5 – Merging Excel Files

    # Trick 6 – Smart Filtering

    # Trick 7 – Mergining Tables

    # Trick 8 – Export Dataframe to Excel

    The whole code with the Excel files is available in GitHub here.

    https://www.youtube.com/watch?v=SXXc4WySZS4

    Enjoy it!



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  • Python – Reading Financial Data From Internet – Useful code

    Python – Reading Financial Data From Internet – Useful code


    Reading financial data from the internet is sometimes challenging. In this short article with two python snippets, I will show how to read it from Wikipedia and from and from API, delivering in JSON format:

    This is how the financial json data from the api looks like.

    Reading the data from the API is actually not tough, if you have experience reading JSON, with nested lists. If not, simply try with trial and error and eventually you will succeed:

    With the reading from wikipedia, it is actually even easier – the site works flawlessly with pandas, and if you count the tables correctly, you would get what you want:

    You might want to combine both sources, just in case:

    The YouTube video for this article is here:
    https://www.youtube.com/watch?v=Uj95BgimHa8
    The GitHub code is there – GitHub

    Enjoy it! 🙂



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  • 2 ways to generate realistic data using Bogus &vert; Code4IT

    2 ways to generate realistic data using Bogus | Code4IT


    Just a second! 🫷
    If you are here, it means that you are a software developer.
    So, you know that storage, networking, and domain management have a cost .

    If you want to support this blog, please ensure that you have disabled the adblocker for this site.
    I configured Google AdSense to show as few ADS as possible – I don’t want to bother you with lots of ads, but I still need to add some to pay for the resources for my site.

    Thank you for your understanding.
    Davide

    In a previous article, we delved into the creation of realistic data using Bogus, an open-source library that allows you to generate data with plausible values.

    Bogus contains several properties and methods that generate realistic data such as names, addresses, birthdays, and so on.

    In this article, we will learn two ways to generate data with Bogus: both ways generate the same result; the main change is on the reusability and the modularity. But, in my opinion, it’s just a matter of preference: there is no approach absolutely better than the other. However, both methods can be preferred in specific cases.

    For the sake of this article, we are going to use Bogus to generate instances of the Book class, defined like this:

    public class Book
    {
        public Guid Id { get; set; }
        public string Title { get; set; }
        public int PagesCount { get; set; }
        public Genre[] Genres { get; set; }
        public DateOnly PublicationDate { get; set; }
        public string AuthorFirstName { get; set; }
        public string AuthorLastName { get; set; }
    }
    
    public enum Genre
    {
        Thriller, Fantasy, Romance, Biography
    }
    

    Expose a Faker inline or with a method

    It is possible to create a specific object that, using a Builder approach, allows you to generate one or more items of a specified type.

    It all starts with the Faker<T> generic type, where T is the type you want to generate.

    Once you create it, you can define the rules to be used when initializing the properties of a Book by using methods such as RuleFor and RuleForType.

    public static class BogusBookGenerator
    {
        public static Faker<Book> CreateFaker()
        {
            Faker<Book> bookFaker = new Faker<Book>()
             .RuleFor(b => b.Id, f => f.Random.Guid())
             .RuleFor(b => b.Title, f => f.Lorem.Text())
             .RuleFor(b => b.Genres, f => f.Random.EnumValues<Genre>())
             .RuleFor(b => b.AuthorFirstName, f => f.Person.FirstName)
             .RuleFor(b => b.AuthorLastName, f => f.Person.LastName)
             .RuleFor(nameof(Book.PagesCount), f => f.Random.Number(100, 800))
             .RuleForType(typeof(DateOnly), f => f.Date.PastDateOnly());
    
            return bookFaker;
        }
    }
    

    In this way, thanks to the static method, you can simply create a new instance of Faker<Book>, ask it to generate one or more books, and enjoy the result:

    Faker<Book> generator = BogusBookGenerator.CreateFaker();
    var books = generator.Generate(10);
    

    Clearly, it’s not necessary for the class to be marked as static: it all depends on what you need to achieve!

    Expose a subtype of Faker, specific for the data type to be generated

    If you don’t want to use a method (static or not static, it doesn’t matter), you can define a subtype of Faker<Book> whose customization rules are all defined in the constructor.

    public class BookGenerator : Faker<Book>
    {
        public BookGenerator()
        {
            RuleFor(b => b.Id, f => f.Random.Guid());
            RuleFor(b => b.Title, f => f.Lorem.Text());
            RuleFor(b => b.Genres, f => f.Random.EnumValues<Genre>());
            RuleFor(b => b.AuthorFirstName, f => f.Person.FirstName);
            RuleFor(b => b.AuthorLastName, f => f.Person.LastName);
            RuleFor(nameof(Book.PagesCount), f => f.Random.Number(100, 800));
            RuleForType(typeof(DateOnly), f => f.Date.PastDateOnly());
        }
    }
    

    Using this way, you can simply create a new instance of BookGenerator and, again, call the Generate method to create new book instances.

    var generator = new BookGenerator();
    var books = generator.Generate(10);
    

    Method vs Subclass: When should we use which?

    As we saw, both methods bring the same result, and their usage is almost identical.

    So, which way should I use?

    Use the method approach (the first one) when you need:

    • Simplicity: If you need to generate fake data quickly and your rules are straightforward, using a method is the easiest approach.
    • Ad-hoc Data Generation: Ideal for one-off or simple scenarios where you don’t need to reuse the same rules across your application.

    Or use the subclass (the second approach) when you need:

    • Reusability: If you need to generate the same type of fake data in multiple places, defining a subclass allows you to encapsulate the rules and reuse them easily.
    • Complex scenarios and extensibility: Better suited for more complex data generation scenarios where you might have many rules or need to extend the functionality.
    • Maintainability: Easier to maintain and update the rules in one place.

    Further readings

    If you want to learn a bit more about Bogus and use it to populate data used by Entity Framework, I recently published an article about this topic:

    🔗Seeding in-memory Entity Framework with realistic data with Bogus | Code4IT

    This article first appeared on Code4IT 🐧

    But, clearly, the best place to learn about Bogus is by reading the official documentation, that you can find on GitHub.

    🔗 Bogus repository | GitHub

    Wrapping up

    This article sort of complements the previous article about Bogus.

    I think Bogus is one of the best libraries in the .NET universe, as having realistic data can help you improve the intelligibility of the test cases you generate. Also, Bogus can be a great tool when you want to showcase demo values without accessing real data.

    I hope you enjoyed this article! Let’s keep in touch on LinkedIn, Twitter or BlueSky! 🤜🤛

    Happy coding!

    🐧





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  • How Data Analytics Can Help You Grow Your Business

    How Data Analytics Can Help You Grow Your Business


    In today’s fast-paced and competitive business landscape, companies must leverage data analytics to better understand their customers, improve products and services, and make informed decisions that can help their business grow. With the right tools and insightful analytics, businesses can identify new opportunities, spot trends, and gain a competitive advantage. In this article, we will explore four ways data analytics can help you grow your business. Keep reading to learn more.

    Boosting Customer Engagement and Loyalty

    Customer Engagement

     

    One of the primary benefits of using data analytics in your business is the ability to better understand your customers. By collecting and analyzing customer data, you can gain insights into their preferences, needs, and behaviors. This information can then be used to create targeted marketing campaigns and personalized experiences that are tailored to their specific interests. As a result, customers will feel more engaged with your brand and are more likely to remain loyal, resulting in higher customer retention rates and increased revenue.

    Additionally, by analyzing customer feedback, businesses can determine what aspects of their products or services require improvement. This can lead to increased customer satisfaction and an even stronger brand reputation. Having access to some of the best data analytics programs can greatly enhance your understanding of your target audience.

    Furthermore, data analytics allows businesses to identify and reach out to potential customers, leading to a more effective acquisition process. By utilizing various channels, techniques, and types of data, businesses can identify potential customers who are most likely to convert, allowing them to maximize their marketing efforts and investments.

    Improving Operational Efficiency

    Data analytics can also be invaluable in improving operational efficiency within a business. By collecting and analyzing data from internal processes, businesses can pinpoint areas of inefficiency, identify bottlenecks, and determine areas that require optimization. This can lead to significant cost savings and better resource allocation, ultimately resulting in improved profitability.

    Data analytics can also be applied to supply chain management, helping businesses optimize their inventory levels, reduce waste, and manage their relationships with suppliers more effectively. This can result in a more streamlined supply chain, leading to improved customer satisfaction and increased revenue.

    Businesses that are involved in transportation or logistics, such as those utilizing Esso Diesel for fuel, can also employ data analytics for optimizing routes and vehicle performance. By analyzing data on fuel consumption, traffic patterns, and driver behavior, businesses can implement more cost-effective and efficient transportation strategies, leading to significant savings and a better environmental footprint.

    Supporting Informed Decision-Making

    Data analytics is a key tool in helping business leaders make more informed and data-driven decisions. Rather than relying on intuition or gut feelings, businesses can use data to analyze past performance, project future trends, and identify patterns to support their decision-making process. This results in better strategic planning and more effective decision-making, enabling businesses to grow and stay ahead of their competition.

    For example, data analytics can be used to identify revenue-generating products or services, allowing businesses to focus their resources on these areas. This can help to consolidate their position within the market and drive growth through targeted investments and expansion.

    Innovating and Developing New Products

    Innovating and Developing New Products

    Lastly, data analytics can support innovation by helping businesses to identify new product development opportunities. By understanding customer needs, preferences, and pain points, businesses can develop products and services that meet the demands of their existing customers, but also attract new ones.

    Furthermore, data analytics can be used to identify emerging trends or unmet needs within the market. By leveraging this information, businesses can position themselves as leaders and innovators within their industry, setting them apart from their competitors and driving growth.

    Data analytics is a powerful tool that can drive growth and improvements across various aspects of a business, from enhancing customer engagement and loyalty to optimizing internal processes, supporting informed decision-making, and fostering innovation. By harnessing the power of data analytics, businesses can set themselves on a path to sustained growth and success.



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  • Digital Personal Data Protection act Guide for Healthcare Leaders

    Digital Personal Data Protection act Guide for Healthcare Leaders


    The digital transformation of India’s healthcare sector has revolutionized patient care, diagnostics, and operational efficiency. However, this growing reliance on digital platforms has also led to an exponential increase in the collection and processing of sensitive personal data. The Digital Personal Data Protection (DPDP) Act 2023 is a critical regulatory milestone, shaping how healthcare organizations manage patient data.

    This blog explores the significance of the DPDP Act for hospitals, clinics, pharmaceutical companies, and other healthcare entities operating in India.

    Building an Ethical and Trustworthy Healthcare Environment

    Trust is the cornerstone of patient-provider relationships. The DPDP Act 2023 reinforces this trust by granting Data Principals (patients) fundamental rights over their digital health data, including access, correction, and erasure requests.

    By complying with these regulations, healthcare organizations can demonstrate a commitment to patient privacy, strengthening relationships, and enhancing healthcare outcomes.

    Strengthening Data Security in a High-Risk Sector

    The healthcare industry is a prime target for cyberattacks due to the sensitivity and value of patient data, including medical history, treatment details, and financial records. The DPDP Act mandates that healthcare providers (Data Fiduciaries) implement comprehensive security measures to protect patient information from unauthorized access, disclosure, and breaches. This includes adopting technical and organizational safeguards to ensure data confidentiality, integrity, and availability.

    Ensuring Regulatory Compliance and Avoiding Penalties

    With strict compliance requirements, the Digital Personal Data Protection Act provides a robust legal framework for data protection in healthcare. Failure to comply can result in financial penalties of up to ₹250 crore for serious violations. By aligning data processing practices with regulatory requirements, healthcare entities can avoid legal risks, safeguard their reputation, and uphold ethical standards.

    Promoting Patient Empowerment and Data Control

    The DPDP Act empowers patients with greater control over their health data. Healthcare providers must establish transparent mechanisms for data collection and obtain explicit, informed, and unambiguous patient consent. Patients also have the right to know how their data is used, who has access, and for what purposes, reinforcing trust and accountability within the healthcare ecosystem.

    Facilitating Innovation and Research with Safeguards

    While prioritizing data privacy, the Digital Personal Data Protection Act also enables responsible data utilization for medical research, public health initiatives, and technological advancements. The Act provides pathways for the ethical use of anonymized or pseudonymized data, ensuring continued innovation while protecting patient rights. Healthcare organizations can leverage data analytics to improve treatment protocols and patient outcomes, provided they adhere to principles of data minimization and purpose limitation.

    Key Obligations for Healthcare Providers under the DPDP Act

    Healthcare organizations must comply with several critical obligations under the DPDP Act 2023:

    • Obtaining Valid Consent: Secure explicit patient consent for collecting and processing personal data for specified purposes.
    • Implementing Security Safeguards: To prevent breaches, deploy advanced security measures, such as encryption, access controls, and regular security audits.
    • Data Breach Notification: Promptly report data breaches to the Data Protection Board of India and affected patients.
    • Data Retention Limitations: Retain patient data only as long as necessary and ensure secure disposal once the purpose is fulfilled.
    • Addressing Patient Rights: Establish mechanisms for patients to access, correct, and erase their personal data while addressing privacy-related concerns.
    • Potential Appointment of a Data Protection Officer (DPO): Organizations processing large volumes of sensitive data may be required to appoint a DPO to oversee compliance efforts.

    Navigating the Path to DPDP Compliance in Healthcare

    A strategic approach is essential for healthcare providers to implement the DPDP Act effectively. This includes:

    • Conducting a comprehensive data mapping exercise to understand how patient data is collected, stored, and shared.
    • Updating privacy policies and internal procedures to align with the Act’s compliance requirements.
    • Training employees on data protection best practices to ensure organization-wide compliance.
    • Investing in advanced data security technologies and establishing robust consent management and incident response mechanisms.

    A Commitment to Data Privacy in Healthcare

    The Digital Personal Data Protection Act 2023 is a transformative regulation for the healthcare industry in India. By embracing its principles, healthcare organizations can ensure compliance, strengthen patient trust, and build a secure, ethical, and innovation-driven ecosystem.

    Seqrite offers cutting-edge security solutions to help healthcare providers protect patient data and seamlessly comply with the DPDP Act.

     



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