What to Look for in a Sensitive Data Discovery Tool: A 2025 Guide
Sensitive data discovery is critical for organizations to protect their data, comply with regulations, and mitigate risks. However, selecting the right tool can be challenging. Here are the five key factors to consider when choosing a sensitive data discovery solution in 2025, optimized for modern needs like scalability, accuracy, and integration. Accuracy: Precision in Identifying Sensitive Data Accuracy is the cornerstone of effective data discovery. The tool you choose must: Distinguish between sensitive and non-sensitive data with minimal false positives or negatives. Leverage AI-driven models to analyze complex patterns and contexts. Ensure sensitive data is neither overlooked nor misclassified, which could lead to compliance risks or operational inefficiencies. Data Privacy: Ensuring Compliance and Trust Sensitive data discovery inherently involves scanning large datasets, which can raise privacy concerns. A robust tool should: Comply with global regulations like GDPR, CCPA, HIPAA, and PCI-DSS. Use encryption during scanning to protect sensitive information. Offer on-premises or hybrid deployment options for organizations with strict privacy requirements. Scalability: Handling Large and Growing Data Volumes As organizations generate more data across multiple environments, scalability is essential. Look for tools that: Support multi-cloud and hybrid environments seamlessly. Handle diverse datasets across structured, unstructured, and semi-structured formats. Scale efficiently without compromising performance or accuracy. Integration: Seamless Compatibility with Existing Systems A good sensitive data discovery tool must integrate smoothly into your existing tech stack. It should: Connect to various databases, cloud storage solutions, and SaaS platforms. Work with newer database technologies and legacy systems alike. Provide APIs for custom integrations and workflows. Cost: Balancing Investment with Long-Term Value Cost is always a concern, especially for large enterprises managing multiple data sources. The ideal tool should: Use AI-driven automation to reduce manual effort and operational costs. Offer flexible pricing models that align with your organization’s scale and requirements. Demonstrate measurable ROI by streamlining compliance processes and reducing risk exposure. Why Choose the C2 Data Privacy Platform? The C² Data Privacy Platform addresses all these key factors effectively: Accuracy: Uses AI-powered discovery to identify sensitive data with precision while minimizing false positives. Privacy: Ensures compliance with major regulations like GDPR, CCPA, HIPAA, and PCI-DSS through secure scanning methods. Scalability: Handles vast amounts of data across multi-cloud environments without performance degradation. Integration: Seamlessly connects to your existing systems, including cloud storage, databases, and SaaS platforms. Cost Efficiency: Automates the discovery process to save time and resources while prioritizing high-risk data for remediation. By leveraging the C² Data Privacy Platform, organizations can confidently address their sensitive data discovery needs while staying compliant, efficient, and secure in 2025’s ever-evolving digital landscape. AWS Storefront
The Limitations of Regex-Based Discovery in Data Security
Regex-based discovery, while useful for simple pattern matching, faces significant challenges in modern data security: Limited Expressiveness: Struggles with complex patterns and context-aware data structures. Data Variability: Difficulty in creating patterns that capture all data variations accurately. False Positives: Rigid matching often leads to irrelevant or incorrect identifications. Maintenance Burden: Requires constant manual updates, time-consuming for large datasets. Performance Issues: Can be computationally expensive, especially for large-scale data analysis. Lack of Context: Unable to understand broader data context, leading to potential inaccuracies. Limited File Support: Only effective for text-based files, missing data in images or non-searchable PDFs. These limitations highlight why regex-based discovery alone is insufficient for comprehensive data security in today’s complex digital environments. How C² Data Privacy Platform Can Help The C² Data Privacy Platform empowers organizations to discover, secure, and manage sensitive data seamlessly across cloud and hybrid environments. Leveraging advanced AI and deep learning, it automates data discovery, classification, and risk assessment, reducing manual errors and improving efficiency. With built-in encryption and integration for masking and other security tools, the platform ensures adherence to regulations like HIPAA, GDPR, CCPA, SOX, PCI DSS, and GLBA. Its user-friendly interface provides actionable insights into exposure risks, enabling proactive data protection. By streamlining data security processes, C² helps customers mitigate breaches, maintain compliance, and build trust in an increasingly complex digital landscape. AWS Storefront
PCI-DSS Compliance: Safeguarding Sensitive Data and Preparing for Audits
The Payment Card Industry Data Security Standard (PCI-DSS), established on September 7, 2006, is a critical framework designed to protect cardholder data. It applies to organizations like banks, healthcare providers, and any entity handling payment card information. Non-compliance can lead to significant penalties, making adherence essential for businesses. Why PCI-DSS Compliance Matters Compliance with PCI-DSS ensures the security of sensitive data such as credit card numbers, authentication data, and personal information. As organizations increasingly operate in cloud environments and handle vast amounts of data, understanding data sensitivity and vulnerabilities is fundamental to compliance. Achieving PCI-DSS compliance involves a comprehensive process that includes risk assessments, monitoring for malicious activities, updating documentation on data flows, and staying current with evolving standards. The PCI-DSS Audit Process A PCI-DSS audit is a thorough examination of your security infrastructure to ensure compliance with the standard. Here’s what the process typically involves: Focus on Sensitive DataAuditors review sensitive data elements such as primary account numbers (PANs), expiration dates, and routing numbers. They identify security gaps and may require remediation to address vulnerabilities. Recommendations for ImprovementAuditors often recommend preventive measures like documenting data flows, updating security policies, and improving access controls to protect cardholder data. Proactive PreparationThink of an audit as a health check-up for your organization’s security posture. Beyond addressing existing concerns, audits ensure proper documentation of sensitive data inventories and protective measures to mitigate risks in case of breaches. Preparing for PCI-DSS Compliance: Key Steps To streamline your audit process and minimize fines or penalties, take these proactive steps: 1. Achieve PCI-DSS Certification Certification requirements depend on transaction volume: Level 1 (6M+ transactions/year): Annual internal audits and quarterly PCI scans. Level 2 (1M-6M transactions/year): Annual risk assessments with Self-Assessment Questionnaires (SAQs) and quarterly scans. Certification involves a tailored risk assessment based on your transaction volume and cloud infrastructure. 2. Conduct Regular Risk Assessments Assess your IT assets and business processes for vulnerabilities. Document all systems involved in storing, processing, or transmitting cardholder data. Regularly update this inventory to reflect changes in your environment. 3. Implement Strong Security Measures Adopt measures such as: Encryption of stored and transmitted cardholder data. Robust firewalls to secure networks. Strong password policies to prevent unauthorized access. Continuous vulnerability monitoring and patch management. 4. Monitor Data Flows Ensure you have clear visibility into how cardholder data moves through your systems. This includes mapping out all connections between payment systems and other components in your network. Why Organizations Struggle with PCI-DSS Compliance Maintaining compliance requires significant investments in time, money, and resources. Challenges include: Managing complex cloud environments. Identifying sensitive data accurately. Keeping up with frequent updates to PCI-DSS standards. Ensuring scalability as transaction volumes grow. Moving Beyond Compliance: The Role of Technology Investing in advanced tools like AI-driven sensitive data discovery platforms can simplify the compliance process by: Automating sensitive data identification. Reducing false positives through context-aware analysis. Scaling seamlessly with growing datasets. Integrating easily with cloud-based storage systems. Final Thoughts PCI-DSS compliance is more than just a regulatory requirement—it’s essential for protecting customer trust and safeguarding sensitive financial information. By proactively preparing for audits, adopting robust security measures, and leveraging advanced technologies, organizations can ensure compliance while minimizing risks. How C² Data Privacy Platform Can Help The C² Data Privacy Platform empowers organizations to discover, secure, and manage sensitive data seamlessly across cloud and hybrid environments. Leveraging advanced AI and deep learning, it automates data discovery, classification, and risk assessment, reducing manual errors and improving efficiency. With built-in encryption and integration for masking and other security tools, the platform ensures adherence to regulations like HIPAA, GDPR, CCPA, SOX, PCI DSS, and GLBA. Its user-friendly interface provides actionable insights into exposure risks, enabling proactive data protection. By streamlining data security processes, C² helps customers mitigate breaches, maintain compliance, and build trust in an increasingly complex digital landscape. AWS Storefront
On-Premises vs. Cloud Data Privacy: Understanding Your Options for a Secure 2025
In today’s data-driven world, organizations grapple with a fundamental decision: Where should sensitive data reside? The choice between on-premise infrastructure and cloud solutions significantly impacts data privacy and security. Regardless of the path chosen, a commitment to robust security measures is non-negotiable. Both on-premise and cloud environments require adherence to stringent regulatory practices like auditing, role-based access control (RBAC), and continuous monitoring. However, the crucial first step remains the same: identifying sensitive data and assessing the associated risks. Without knowing where sensitive data resides and understanding its vulnerabilities, any data protection strategy is inherently flawed. The Core Challenge: Finding and Classifying Sensitive Data The biggest hurdle in modern data privacy is accurately locating and classifying sensitive data across the organization. This presents several challenges: Time-Intensive Process: The sheer volume of data in today’s businesses makes manual discovery impractical. Human Error: Relying on manual processes introduces the risk of overlooking sensitive data or misclassifying it, leading to vulnerabilities. Tool Limitations: Many traditional data discovery tools struggle to look beyond surface-level attributes, failing to analyze the contents of documents and other unstructured data sources. Overcoming these challenges is paramount to building a solid data privacy foundation. Navigating the On-Premise vs. Cloud Landscape When choosing between on-premise and cloud data storage, several factors must be carefully considered: Feature On-Premise Cloud Control Full control over infrastructure, security configurations, and data access. Shared responsibility model; control is distributed between the organization and the cloud provider. Security Requires in-house expertise to configure and maintain security measures. Relies on the cloud provider’s security measures, requiring careful evaluation of their security posture. Scalability Scaling requires significant capital expenditure and lead time. Offers on-demand scalability, but costs can fluctuate based on usage. Compliance Organizations are directly responsible for meeting compliance requirements. Cloud providers offer compliance certifications, but organizations are ultimately responsible for ensuring data is handled correctly. Accessibility Typically accessed via internal networks, limiting exposure. Accessible over the internet, requiring strong authentication and access controls. Cost High upfront capital expenditure, but predictable operating costs. Lower upfront costs but variable operating costs that depend on usage and storage volume. Data Governance Direct control over data governance policies and procedures. Requires careful configuration and oversight to ensure data governance policies are enforced. Disaster Recovery Requires investment in backup and recovery systems. Cloud providers offer built-in disaster recovery capabilities, but organizations need to ensure they meet specific RTO/RPO goals. Data Residency Data resides within the organization’s physical premises. Data may reside in different geographic locations, raising data sovereignty concerns. Expertise Requires internal expertise in infrastructure management, security, and compliance. Reduces the need for in-house expertise but requires a clear understanding of the cloud provider’s responsibilities. Increasingly, organizations are adopting hybrid cloud strategies to combine the benefits of both on-premise and cloud solutions. Understanding the specific needs and risk tolerance of your organization is essential in making the right choice. Building a Solid Data Privacy Framework Regardless of your infrastructure choice, the following steps are crucial for building a robust data privacy framework: Comprehensive Data Discovery: Implement a data discovery process that identifies all sensitive data, regardless of its location or format. Data Classification: Classify data based on its sensitivity level and regulatory requirements. Access Controls: Implement strict access controls to limit access to sensitive data to authorized personnel only. Data Loss Prevention (DLP): Deploy DLP solutions to prevent sensitive data from leaving the organization’s control. Encryption: Encrypt sensitive data both at rest and in transit to protect it from unauthorized access. Monitoring and Auditing: Continuously monitor data access and usage and conduct regular audits to identify potential security breaches. Training and Awareness: Educate employees about data privacy policies and best practices to foster a culture of security awareness. Incident Response: Develop a comprehensive incident response plan to handle data breaches and other security incidents. Moving Forward: A Proactive Approach to Data Privacy Protecting sensitive data is a complex and ongoing process that requires a proactive and comprehensive approach. By carefully considering your infrastructure options, understanding the challenges of data discovery, and implementing a solid data privacy framework, you can safeguard your organization’s data and maintain the trust of your customers. Rather than focusing on a specific product, this approach focuses on providing valuable information and insights, helping the reader make informed decisions about their data privacy strategy. How C² Data Privacy Platform Can Help The C² Data Privacy Platform empowers organizations to discover, secure, and manage sensitive data seamlessly across cloud and hybrid environments. Leveraging advanced AI and deep learning, it automates data discovery, classification, and risk assessment, reducing manual errors and improving efficiency. With built-in encryption and integration for masking and other security tools, the platform ensures adherence to regulations like HIPAA, GDPR, CCPA, SOX, PCI DSS, and GLBA. Its user-friendly interface provides actionable insights into exposure risks, enabling proactive data protection. By streamlining data security processes, C² helps customers mitigate breaches, maintain compliance, and build trust in an increasingly complex digital landscape. AWS Storefront
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.
Complying with Data Privacy Regulations
Ensuring Compliance with Data Privacy Regulations In today’s data-driven environment, protecting sensitive information is crucial. C² Data Technology provides solutions to the significant challenges businesses face in adhering to data privacy regulations. Our objective is to provide you with the necessary tools and expertise to effectively navigate this intricate landscape. Data privacy regulations play a vital role in safeguarding individuals’ personal information, whether they are EU citizens, residents of California, or holders of financial and health data, from threats like data breaches, malware, ransomware, and more. Despite having robust policies in place, the risk of breaches remains. It is essential for organizations to have a clear understanding of the location of their sensitive data, which may be found in unexpected places beyond traditional storage locations. At C² Data Technology, we specialize in offering solutions that provide comprehensive visibility into your data ecosystem. Our advanced technologies empower you to identify and safeguard sensitive data wherever it may be, ensuring compliance and enhancing your overall security posture. By partnering with us, you not only gain peace of mind but also the confidence to innovate and thrive in today’s data-driven economy. Allow us to guide you through the intricacies of data privacy and security so that you can concentrate on what truly matters—your business’s success and earning the trust of your stakeholders. Introducing C² Data Privacy Platform Presenting the C² Data Privacy Platform—a robust solution designed to provide organizations with unparalleled visibility into the location of sensitive data across the entire enterprise, along with advanced data protection measures. C² Manage Access comprehensive visibility into all data regions within your AWS account with C² Manage. This capability forms a solid foundation for extensive data discovery, answering the critical question: “Where is my data stored?” Efficient management of AWS accounts also enables cost optimization, enhancing operational efficiency. C² Discover Utilizing state-of-the-art technologies such as machine learning, AI, and contextual knowledge, C² Discover excels at identifying sensitive data across various enterprise data connections. It meticulously locates sensitive data, even in the most remote corners of your data ecosystem. C² Secure Employing advanced technologies such as machine learning, AI, and contextual knowledge, C² Discover excels at identifying sensitive data across diverse enterprise data connections. It meticulously locates sensitive data, even in the most remote corners of your data ecosystem. A Comprehensive Regulatory Solution For a fool-proof approach to compliance initiatives, apply precise roles and policies to protect your data, ensuring seamless adherence to regulations. At C² Data Technology, we grasp the complexities of modern data environments. Our C² Data Privacy Platform enables organizations to navigate these challenges with assurance. Gain clarity, ensure compliance, and reinforce your data security strategy with C² Data—your proactive partner in comprehensive data privacy management. Moreover, our platform connects you with legal professionals and privacy experts specializing in data protection. Their expert guidance guarantees that your organization achieves full compliance, reduces legal risks, and strengthens your overall data governance framework. Selecting C² Data means empowering your organization with robust data privacy solutions to thrive in today’s dynamic regulatory landscape. At C² Data Technology, we firmly believe that complying with data privacy regulations goes beyond a mere checkbox exercise—it’s an opportunity to demonstrate your dedication to customers and their privacy. Prioritizing data privacy not only upholds ethical standards but also enhances your organization’s reputation. Do not allow data privacy regulations to overwhelm you. Embrace the journey with us at C² Data Technology. Let C² Discover become your trusted partner along the way. Request a demo today and witness how our powerful tools can enhance your data privacy compliance efforts. Together, we can establish a more secure and trustworthy digital future.
Find Your Risk, Protect Your Risk
In today’s complex 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.
The Problem with Regex-Based Discovery
Unlocking the Potential: Beyond the Limitations of Regex-based Discovery In today’s data-driven world, regex-based discovery has proven to be a valuable tool for pattern matching. However, it’s important to recognize the challenges that come with it, as patterns Unlocking the Potential: Beyond the Limitations of Regex-based Discovery In today’s data-driven world, regex-based discovery has proven to be a valuable tool for pattern matching. However, it’s important to recognize the challenges that come with it, as patterns…
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.
