Modern Methods for Managing Data Security
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The document discusses the evolution of data security, highlighting the inadequacies of traditional perimeter-based methods due to increasing data volumes and complexities. It emphasizes a shift towards modern, scalable solutions such as zero-trust architecture and data-centric security, leveraging AI and machine learning for enhanced protection and automation. The importance of classifying and managing shadow data is also addressed, underscoring the risks and potential insights that can be gained from underutilized data.
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Modern Methods for Managing Data Security
- 1. Modern Methods for Managing Data Security Joe Hilger & Ian Thompson
- 2. Agenda 1. Current Data Landscape 2. Traditional Security Methods 3. Shift to Automated and Scalable Data Security 4. Key Components of Modern Data Security ⬢ Architecture ⬢ Solutions 5. Advanced Solutions 6. Q&A 1
- 3. ENTERPRISE KNOWLEDGE I. Current Data Landscape 2 Technological Advances Innovations such as cloud computing, quantum computing, developments in AI are reshaping data landscape and forcing companies to rethink how they secure their data assets Exponential Growth in Data Volume 2010: 2 trillion GB data worldwide 2024: 147 trillion GB data worldwide Due to digital transformation, IoT, social media and big data analytics Data as a product Data Diversity and Complexity Types of Data being collected are more varied than ever, including structured, unstructured, and semi-structured data. Each requiring different storage, processing and protection methods
- 4. Cyber Threats & Data Breaches Trust, Reputation & Economic Impacts Regulatory & Legal Implications Importance of Data Security in the Modern Era Confidentiality Integrity Availability Security Objectives 3
- 5. Articulate the challenges with traditional security methods and how it impacts organizations Today’s Objective Explore more modern approaches to securing your data and scalable solutions to support data security 4
- 6. ENTERPRISE KNOWLEDGE II. Traditional Security Methods Perimeter-Based Security Focused on securing the network perimeter, operating under the assumption that everything inside the network could be trusted. ⬢ Firewalls ⬢ Intrusion detection systems (IDS) ⬢ Intrusion prevention systems (IPS) ⬢ VPN ⬢ Password-Based Authentication VPN 5
- 7. Database Application Server / API Layer API Applications High-Level Perimeter Based Security VPN 6
- 8. ENTERPRISE KNOWLEDGE Erosion of Perimeter Networks VPN Limitations and BYOD Limitations of Traditional Security Methods Cloud Service Complexity Volume Veracity Velocity Variety 7 DATA
- 9. Data Volume & Complexity Due to exponential data growth, manually managing and securing data becomes impractical. Automated solutions can process and protect large volumes of data efficiently. Rapid Detection & Response Volume and speed of modern cyber threats require security systems that can quickly identify and respond to threats Dynamic & Distributed Environments Cloud-Centric and mobile first business environment requires security measures that can adapt to changes in real-time. ENTERPRISE KNOWLEDGE III. Shift to Modern & Scalable Data Security Methods 8
- 10. IV. Key Components of Modern Data Security Shadow / Dark Data Discovery ● What is Shadow / Dark Data? ● Strategies for uncovering and resolving shadow data issues. AI/ML in Data Security ● Detecting and protecting sensitive data. ● Real-world applications Data-Centric Security & Classification ● Security at the Data Level ● Effectively Classifying and Securing Data Zero-Trust Architecture ● Why Zero Trust is essential for modern data security. ● Steps for adopting Zero Trust in an organization. Zero-Trust Architecture AI/ML in Data Security Shadow / Dark Data Discovery Data-Centric Security & Classification Architecture Advanced Solutions 9
- 11. Least privileged access, network segmentation, continuous verification of users and devices Zero Trust Architecture No device is automatically trusted 10 Security controls authenticate every application, device and user Components: Identity and Access Management (IAM), encryption
- 12. Zero Trust Architecture 11 Authentication Signals Verify All Requests Applications & Data Identity Provider (IdP) User & Location Device Application Access Denied Access Granted
- 13. Implementing Zero Trust Architecture ⬢ Ideal environments include highly distributed networks, cloud environments, and remote work settings ⬢ Organizations with a need to protect intellectual property and customer data ⬢ Industries with strict regulatory requirements, such as finance, healthcare, and government sectors 12 ⬢ Assess current network and data access policies ⬢ Identify sensitive data and systems that require protection ⬢ Implement robust identity verification processes ⬢ Continuously monitor and log network activity How? Who? Why?
- 14. Data Centric Security & Data Classification ⬢ Data-centric security focuses on securing the data itself rather than the network or devices where the data resides. ⬢ Data classification is the process of categorizing data based on its level of sensitivity, regulatory requirements, or business needs. ⬢ Strategies such as encryption, tokenization, data masking, and rights management can be applied to the data to protect it throughout its lifecycle. ⬢ Data can be classified into various categories such as public, internal, confidential, or highly confidential, to apply appropriate security measures. 13 At Rest In Transit In Use Data Centric Security Protects Data…
- 15. Implementing Data Centric Security and Data Classification Considerations ⬢ Develop a comprehensive data classification policy. ⬢ Using technical solutions that support data discovery, classification, and protection ⬢ Regular audits and updates to the classification as the data environment evolves Ideal Environments ⬢ Environments with large volumes of sensitive or regulated data, such as PII, financial, or healthcare ⬢ Organizations operating across multiple jurisdictions/regions with varying data protection laws ⬢ Any environment where data is stored or process across diverse platforms and devices, needing to focus on the data itself for security. 14
- 16. V. Advanced Solutions to Support Scalability Role of AI/ML in Data Security Dark Data Classification 15
- 17. AI/ML Roles in Data Security Increased Threat Detection and Response Automated Decision Making in ZTA Risk Assessment Data Protection and Privacy User Behavior & Network Analytics for Anomaly Detection 16
- 18. Identify Shadow / Dark Data ⬢ Description: Shadow data, often also referred to as dark data, typically refers to the information organizations collect, process, and store during regular business activities, but fail to use for other purposes. ⬢ Underutilization: Despite being collected and stored, this data isn't actively used in decision-making or analytics. ⬢ Potential Value: Shadow / Dark Data can potentially hold valuable insights that could benefit business strategy, operational efficiency and risk management if analyzed and used correctly ⬢ Risks and Challenges: Storing this unused data can have its risks and challenges, including increased storage costs, data privacy issues, and security vulnerabilities ⬢ Management Strategies: It is recommended to develop strategies for managing shadow / dark data. Such as... o Data Governance frameworks o Data Audits o Using analytics to uncover hidden and unprotected data 17
- 19. Data Classification ● Categorize organizational data assets based on sensitivity, criticality and usage ● Typical classes: public, internal, confidential, restricted Dark Data Discovery ● Define organizational data categories ● Define data class for each category ● Classify data asset using AI/ML and rules ● Flag sensitive content shared broadly for security review ● Remediate access to overshared sensitive content Data Classification -> Dark Data Discovery 18
- 20. Customer Support 05 ● Use a multi-class classification algorithm to classify customer tickets by predefined categories such as complain, feedback, question and so on ● Integrate classifier output with ticket workflow so that customer support agents can focus on urgent tickets Content Moderation 04 ● Content generated using Gen AI tools must be effectively moderated ● Manual content moderation can be both time-consuming and flawed ● AI-powered content moderation can reduce this cognitive load Survey Analysis 03 ● Train a AI/ML model to categorize qualitative survey responses into predefined categories such as usability, technical complexity if the survey is for an application as an example ● Use the model to categorize survey responses at scale and direct each group of responses to the right team for further analysis Search and Discovery 02 ● Tag content using predefined categories ● Index tag and other metadata about the content into a search engine to power search and discovery Records Management 01 ● For all content without an assigned classification, auto-assign content type ● Assign record codes based on the organization’s record schedule for that content type Data Classification / Dark Data Use Cases 19
- 21. Conclusions Risks of Traditional “Network Perimeter” Security Methods ⬢ 4 V’s of Big Data (Volume, Variety, Velocity & Veracity) impact… ⬢ Perimeter Networks ⬢ Cloud Service Complexity ⬢ VPN Limitations and BYOD Modern Security Methods ⬢ Zero Trust Architecture - Mitigates challenges of perimeter based security by enforcing strict identity verification and continuous monitoring, ensuring that trust is never assumed ⬢ Data Centric / Data Classification - Focus on protecting the data itself, regardless of its location or movement, by applying granular access controls and robust encryption based on data sensitivity. Solutions to Support Data Security Scalability ⬢ Role of AI/ML to automate decision making, risk assessment, anomaly detection and increase threat detection response ⬢ Dark Data discovery to identify and correctly classify large amounts of hidden data to fill in data privacy gaps 20
- 22. Q&A Thank you for listening. Questions?