What to look for in a sensitive data discovery tool
Organizations have common concerns about sensitive data discovery. They include, costs, accuracy, data privacy, scalability and integration with existing systems. As experts in this field we believe any solution must address each of these concerns effectively and efficiently. Accuracy One of the primary concerns with sensitive data discovery is the accuracy of the results. The tools used to identify data must be able to accurately distinguish between sensitive and non-sensitive data, avoiding false positives and negatives which can result in non-sensitive data being flagged as sensitive or worse by having sensitive data being overlooked. Data Privacy Sensitive data discovery involves scanning data stores and systems to identify sensitive information, which can raise privacy concerns. Organizations need to ensure that the software products they are using comply with data protection regulations and that sensitive data is not being exposed. Scalability For large enterprises with vast amounts of data, sensitive data discovery can be a time-consuming and resource-intensive process. Organizations need to ensure that their sensitive data discovery software can scale to accommodate growing amounts of data and are supported by a repeatable well defined process. Integration with Existing Systems Sensitive data discovery tools need to search data in various different systems, including those using cloud-based storage and newer database platforms. Data discovery must work seamlessly with all your existing systems in order to ensure data privacy has been implemented correctly. Cost The cost of implementing sensitive data discovery tools and processes can be significant, particularly for larger organizations with numerous data sources. However the benefit of using AI technology to help control these costs will save time and money. Meet C² Data Privacy With C² Data Privacy, you can easily connect to existing systems, find and identify sensitive data within your data, understand what was found, and apply the discovered insights to your data privacy initiatives to comply with regulations. C² Data Privacy connects to your data sources and scans for sensitive information using machine learning and artificial intelligence to provide users with thorough and accurate results. To avoid false positives and negatives, C² Data Privacy uses multiple layers of analysis to determine whether a data element is truly sensitive. The interactive user interface provides visual aids to help you understand the specifics of what sensitive data was found. Viewing the results at the data source level or at the individual element level helps you focus on what is most important. Once you have identified your sensitive data you can decide how you want to secure it. You can use C² Data Privacy encryption feature or feed what you learned into your preferred data privacy tool to comply with regulations such as Gramm Leach Bliley, HIPAA, CCPA, GDPR, and PCI-DSS.
The Problem with Regex-Based Discovery
Regex Pro’s/Con’s Regex-based discovery looks at the sequence of characters that specifies a match pattern in the text. While regex-based discovery can be a powerful tool for pattern matching, it also faces several challenges. Complex Patterns Regex-based discovery can become increasingly complex and difficult to design as the pattern requirements become more intricate. Complex patterns may require nested or conditional expressions, making them harder to create and maintain. Limited Expressiveness Regex-based discovery has limited expressiveness compared to more advanced programming languages or machine-learning models. They may struggle to handle certain types of patterns or data structures that require context awareness or more sophisticated logic. Data Variability If the data being analyzed has high variability or in consistency in its structure or formatting, creating a single regex pattern that captures all variations can be challenging. Adapting regex patterns to accommodate different cases can lead to increased complexity and reduced accuracy. Overfitting and False Positives Regex-based discovery patterns are specific and rigid, matching only the exact pattern they are designed for. This can result in overfitting, where the pattern matches irrelevant data, or false positives, where the pattern mistakenly identifies incorrect matches. Maintenance and Updates Regex patterns require manual creation and maintenance by human experts. If the underlying data changes or new patterns emerge, regex patterns need to be updated accordingly. This can be time-consuming and error-prone, especially when dealing with large-scale or dynamic datasets. Performance Issues Complex regex-based discovery can be computationally expensive and slow down the data analysis process, particularly when applied to large datasets. In some cases, nested or recursive patterns may cause performance degradation. Lack of Context Understanding Regex patterns are unstable to capture contextual information beyond the defined pattern. They may struggle to interpret and understand the broader context in which the pattern occurs, leading to potential inaccuracies or missed matches.
Unlock the Power of Machine Learning Discovery
Discover a world of limitless possibilities with machine learning for sensitive data discovery. Say goodbye to the limitations of regex-based discovery, which relies on predefined patterns and hello to the adaptability and power of machine learning models. These models have the ability to learn and adapt to even the most complex patterns, opening up endless opportunities for your business. As data complexity increases, regex falls short in expression. With machine learning models, complexity is no longer a challenge. They excel in handling intricate data structures, making them ideal for dealing with more complex patterns. Effortlessly scale your data analysis with machine learning discovery. Regardless of dataset size or diversity, machine learning models can handle vast amounts of data and be trained on various data types, ensuring accurate and efficient analysis. Bid farewell to the days of time-consuming manual pattern design. Machine learning automates the pattern extraction process, reducing human effort once the model is trained. This frees up valuable time and resources, so you can focus on other crucial tasks. Accuracy reigns supreme in data analysis. While regex-based discovery may be accurate for simple patterns, machine learning takes it a step further. By learning patterns from data, models can achieve high accuracy, which is crucial for insightful data analysis. In summary, machine learning discovery offers unparalleled versatility, flexibility, and scalability. Embrace the power of machine learning to unlock limitless insights and opportunities for your business. Say goodbye to limitations and hello to a new era of data analysis with unshakable confidence.
Introducing Bias-Aware Machine Learning: A Paradigm Shift in Decision-Making
In the realm of machine learning, bias has always been a constant concern. Algorithms, though designed to assist in making decisions faster and more accurately, are not immune to biases. But fear not, because, at C² Data, we have revolutionized the landscape with our bias-aware machine learning models. Machine learning bias, as Tech Target elucidates, occurs when algorithms produce results that are inherently biased. This bias is often derived from the training process and the algorithm’s configuration. Let’s delve deeper into the different types of biases encountered: Algorithm Bias: Whether due to faulty algorithms or incompatibility with specific scenarios or software, this bias misinforms users, leading to erroneous outcomes. Sample Bias: The data used to train and test machine learning models may contain errors. Issues arise when the dataset is either too large, too small, or lacks diversity. Striving for the optimal balance in size and diversity is a challenge when testing the model. Prejudice Bias: Just like humans, machine learning models can develop prejudice bias based on the datasets reflecting inherent prejudices and stereotypes. Measurement Bias: Accurately measuring results demands meticulous attention. Any issues faced during this process can skew measurements, causing bias in the output. Exclusion Bias: Intentionally excluding certain data points can create skewness or bias within the machine learning model, undermining its efficacy. So, how does C² Discover come to your rescue? Carefully selecting and preprocessing the training data:At C² Discover, we have applied real-world schemas to generate synthetic data that perfectly matches real-world scenarios. This approach ensures that our training data remains representative and free from bias or outliers found within sensitive fields. Implementing fair and robust decision-making processes:Unlike traditional models, we incorporate a multi-model approach, amalgamating different models to make final decisions regarding sensitive data. By considering a broad range of perspectives, we ensure fairness and robustness in our decision-making process. Regularly evaluating the model’s performance:C² Discover continuously measures the performance of our models across various datasets. We meticulously evaluate outputs to pinpoint any potential sources of bias and make necessary adjustments to mitigate them. With C² Discover’s bias-aware machine learning, you can confidently embrace a paradigm shift in decision-making. Make informed choices without the shackles of biases that plague traditional algorithms. Embrace the future of machine learning today! Discover how our groundbreaking solutions can unlock the true potential of your data by clicking the button below.
Find Your Risk, Protect Your Risk
In today’s intricate corporate data landscape, complexity arises from the multitude of applications and teams needing access to data. This often leaves organizations uncertain about the location of their sensitive data and consequently, unaware of the risks they face in terms of compliance with regulatory standards. Our Comprehensive Solution Introducing the C² Data Privacy Platform, a robust solution designed to empower organizations with clear visibility into the whereabouts of sensitive data across the entire enterprise. C² Manage With C² Manage, users gain comprehensive visibility into all data regions within their AWS account, establishing a solid foundation for thorough data discovery. This capability directly addresses the fundamental question: “Where is my data stored?” Additionally, C² Manage enables cost optimization through efficient AWS account management. C² Discover Powered by advanced techniques such as machine learning, AI, and contextual knowledge, C² Discover excels in identifying sensitive data across various enterprise data connections. It precisely pinpoints the exact locations where sensitive data resides, even in less visible areas of your data ecosystem. C² Secure Ensuring data security is a top priority, and C² Secure offers a range of robust options including encryption, masking, synthesis, and redaction. With over 21 years of experience serving Fortune 500 clients, C² Secure provides the assurance that sensitive data is effectively safeguarded. With the C² Data Privacy Platform, organizations can confidently navigate the complexities of modern data environments. Enhance compliance, gain clarity, and strengthen your data security strategy with C² Data – your proactive partner in data privacy management.
Finding Sensitive Data
At C² Data Technology, we aim to find sensitive data in places where it’s not obvious. Practically, we seek to locate and classify sensitive entities in your data repositories. Using machine learning, we detect over 35 types of sensitive data, covering the bases for HIPPA, PII, and national and international regulations using machine learning. This post will focus on what makes C² Discover the next-generation tool to detect and monitor sensitive data. What Is the Common Approach to Detecting Sensitive Data? The most common approach is rule-based, as it relies mainly on hand-crafted rules with a foundation in regular expressions. Rules can be designed based on domain-specific labels and syntactic-lexical patterns. Regex can work well with the lexicon is exhaustive. However, it’s impossible to cover all patterns due to domain-specific rules and incomplete dictionaries. Take entity “address” for example. It’s next to. Impossible to include all patterns for varied address formats around the work and it relies heavily on manual effort to construct. Regex’s don’t work when the data doesn’t follow any known rules! How Does C² Discover Develop a Next-Generation Solution? By tapping into the breadth and depth of machine learning algorithms and innovative cloud technologies, C² Data came up with a hybrid Machine Learning model. We call our solution C² Discover’s exclusive Deep Learning based model. It uses a combination of machine learning resources powered by AWS (e.g., AWS Comprehend) and additional layers of contextual rules based on our experience. The results based on these combined methods provide a higher degree of accuracy than either one alone. How does C² Discover Detect Sensitive Data? Reducing the Human Effort Traditional rule-based approaches require a considerable amount of engineering skills and domain expertise. Applying deep learning-based models, on the other hand, is effective in automatically learning representations and underlying factors from raw data. C² Discover will save significant effort in designing rules and writing regex expressions as well as adapting quickly to new data environments. Employing Rich Features in Model Training By sourcing synthetic data based on the real-world schema, we were able to build C² Discover’s exclusive learning-based model. We incorporated not only world-level and character-based representation learned from an end-to-end neural model, but also additional information (e.g., gazetteers and linguistic dependency). These rich features allow our model to have a better understanding of different data repositories. Applying Weighted Results By combining different resources results, C² Discover’s robustness is guaranteed. In this way, bias can be hugely decreased by using C² Discover than other solutions that depend on one model only.
Are Cloud Providers Responsible for All Aspects of Data Security?
What Is a Cloud Provider Cloud providers are third-party companies that offer a cloud-based platform, infrastructure, application, or storage services. Their responsibilities typically cover the following: Certifications and standards Technologies and service roadmap Data security, data governance, and business policies Service dependencies and partnerships Contracts, commercials, and SLAs (Service Level Agreements) Reliability and performance Migration support, vendor lock-in, and exit planning Business health and company profile Cloud Providers and castles have many similarities. They both host and have a standard protection system, like keys and locks, doors, a moat, etc. The responsibility falls on the users and the royal family that is using them. Like any attacker on a castle, talented hackers can get into the infrastructure at any time. The entire system is ruined if they don’t protect what’s inside. How Can the C² Data Privacy Platform Help? The C² Data Privacy Platform is your all-in-one solution for managing and securing data across enterprise cloud and hybrid environments. It handles data management, discovery, and security with ease. Key Features: C² Manage: Gain full visibility into all data regions within your AWS account, laying the foundation for comprehensive data discovery by answering the crucial question: “Where is my data stored?” C² Discover: Leverage cutting-edge data discovery techniques, including machine learning, AI, and contextual knowledge, to accurately analyze and identify sensitive data across various sources. C² Discover provides a unified view of data locations, highlights areas with high concentrations of sensitive information, and assigns the risk scores based on what types and how much sensitive data was found. C² Secure: Enhance your data security posture and mitigate the impact of breaches. With over 21 years of experience serving Fortune 500 clients, C² Secure offers expert recommendations on data encryption, masking, synthesis, and redaction to effectively protect sensitive data.
Challenges of Machine Learning-Based Data Discovery
Machine learning is more accurate than regex. In fact, many data discovery tools claim to use machine learning when combing through data environments, looking for sensitive data. However, it may face several challenges. Data Bias and Fairness Machine learning models are sensitive to biases present in the training data. If the training data contains biased or unrepresentative samples, the model can learn and perpetuate the biases, leading to unfair or discriminatory outcomes. Ensuring fairness and mitigating bias in machine learning models is a critical challenge. Data Privacy and Security Machine learning models often require access to sensitive or private data. Protecting the privacy and security of such data. Protecting the privacy and security of such data during training and deployment is essential. Adversarial attacks, data breaches, or unintended information leakage can pose significant risks. Data Processing and Cleaning Preparing the data of machine learning often involves data preprocessing, including handling missing values, outliers, and inconsistent formatting. These tasks can be time-consuming and require domain knowledge and expertise. Interpretability and Explainability As models become more complex, their interpretability and explainability diminish. Understanding and interpreting the decision made by a model can be challenging, which can hinder trust and acceptance, particularly in critical domains such as healthcare or finance. Lack of Transparency The models can be viewed as black boxes, making it difficult to understand their internal workings. This lack of transparency can lead to skepticism and resistance, especially in scenarios where explainability is required, such as regulatory compliance or auditing. Data Quantity and Quality Machine learning models typically require large amounts of high-quality labeled data for effective training. However, obtaining labeled data can be very expensive, time-consuming, and in some cases, practically infeasible. Limited or low-quality data can adversely affect model performance and generalization. Model Robustness and Adversarial Attacks Machine learning models can be vulnerable to adversarial attacks, where malicious actors intentionally manipulate inputs to mislead or exploit the model. Ensuring robustness against such attacks is crucial, especially in safety-critical applications like autonomous vehicles or cybersecurity. Addressing these challenges requires a comprehensive and thoughtful approach, encompassing data collection and preprocessing practices, model selection and training, robust evaluation methodologies, and ethical considerations throughout the entire machine learning pipeline. How Is C² Data Privacy Platform’s Data Discovery Different C² Discover’s data discovery methods don’t rely on machine learning only. C² Discover leverages AI, contextual knowledge from our extensive experience protecting Fortune 500 companies, and machine learning to deliver accurate data discovery results. Our “data first, metadata second” approach ensures direct data analysis, using surrounding data to confirm and enhance our findings right out of the box. Understand what was found using the interactive, drill-down user interface. C² Discover displays the landscape of where the risk lies throughout the environment through the sensitive data landscape. Drill down to the source level to see what and where the highest concentrations of sensitive data are. The percentage breakdown is also displayed for users to view.
C² Discover 3.0 released Sensitive Data Landscape
Today we are excited to announce some major advancements to the C² Discover product that provide users with a deeper understanding of their sensitive data across the enterprise. This includes a new Sensitive Data Landscape, Improved Data Risk Monitoring, Improved Discovery Performance, and Extended File Support. Sensitive Data Landscape This feature shows your data exposure risk across all your data sources. Based on our proprietary risk scoring algorithms the sensitive data landscape shows you what types of sensitive data are stored in your databases and files as well as the potential risk it represents. The goal is to provide insight into your enterprise’s risk so you can determine how best to protect your data. C² Discover calculates risk by analyzing the data discoveries you have run as well as how many other data sources you have but have not yet scanned. C² Discover tells you what types of sensitive data you have such as personal identifiable data, financial data, and healthcare or HIPAA data. Improved Data Risk Monitoring Get even closer and see how your risk is trending over time. C² Discover shows if your sensitive data increased, decreased or stayed the same on a monthly, quarterly, or annual basis. This tells you when and where to act to secure your data before it is too late. Improved Discovery Performance With C² Discover 3.0, the scanning process has been made smarter so you get your results faster. C² Discover already provided sophisticated sampling features to allow you to control how you search for your sensitive data. Some of our larger clients asked us to improve the speed of the Discoveries for large or complex databases as well as hundreds or even thousands of data sources. We listened and improved how C² Discover does parallel sampling. One more step to making C² Discover the best solution for large enterprises! Smarter File Support Large customers have legacy systems that use older file types. C² Discover now supports EDI files, both X12 and EDIFACT and can discover sensitive data in individual worksheets in Microsoft Excel (XLS, XLSX). C² Discover scans all your files and has built-in intelligence to identify common industry file types to improve the accuracy of the results. C² Discover is your enterprise-level cloud solution. C² Discover connects to your cloud-native data sources, whether it’s relational databases, NoSQL, S3, data lakes, or warehouses, and discovers sensitive data. Our approach to sensitive data discovery uses our deep learning technology, utilizing machine learning models and contextual knowledge based on our unmatched experience with data privacy experience. Visually understand your risk across your enterprise, what and where the sensitive data was found.
