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Interview Questions on Data Classification and DLP Data Loss Prevention
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- čas přidán 3. 01. 2021
- Interview Questions on Data Classification and DLP Data Loss Prevention
Data classification is the process of analyzing structured or unstructured data and organizing it into categories based on file type, contents, and other metadata
Data classification is broadly defined as the process of organizing data by relevant categories so that it may be used and protected more efficiently.
Data Sensitivity Levels
REASONS FOR DATA CLASSIFICATION
TYPES OF DATA CLASSIFICATION
USING A DATA CLASSIFICATION MATRIX
AN EXAMPLE OF DATA CLASSIFICATION
THE DATA CLASSIFICATION PROCESS
What is DLP?
Data loss prevention (DLP), per Gartner, may be defined as technologies which perform both content inspection and contextual analysis of data sent via messaging applications such as email and instant messaging, in motion over the network, in use on a managed endpoint device, and at rest in on-premises file servers or in cloud applications and cloud storage
How does DLP work?
Once the envelope is opened and the content processed, there are multiple content analysis techniques which can be used to trigger policy violations, including:
Rule-Based/Regular Expressions: The most common analysis technique used in DLP involves an engine analyzing content for specific rules such as 16-digit credit card numbers, 9-digit U.S. social security numbers, etc. This technique is an excellent first-pass filter since the rules can be configured and processed quickly, although they can be prone to high false positive rates without checksum validation to identify valid patterns.
Database Fingerprinting: Also known as Exact Data Matching, this mechanism looks at exact matches from a database dump or live database. Although database dumps or live database connections affect performance, this is an option for structured data from databases.
Exact File Matching: File contents are not analyzed; however, the hashes of files are matches against exact fingerprints. Provides low false positives although this approach does not work for files with multiple similar but not identical versions.
Partial Document Matching: Looks for complete or partial match on specific files such as multiple versions of a form that have been filled out by different users.
Conceptual/Lexicon: Using a combination of dictionaries, rules, etc., these policies can alert on completely unstructured ideas that defy simple categorization. It needs to be customized for the DLP solution provided.
Statistical Analysis: Uses machine learning or other statistical methods such as Bayesian analysis to trigger policy violations in secure content. Requires a large volume of data to scan from, the bigger the better, else prone to false positives and negatives.
Pre-built categories: Pre-built categories with rules and dictionaries for common types of sensitive data, such as credit card numbers/PCI protection, HIPAA, etc.
They are, from highest to lowest:
Restricted Data/Formerly Restricted Data
Code Word classification
Top Secret
Secret
Confidential
Public Trust
Controlled Unclassified Information (CUI) #CyberSecurity
Lovely information
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Thank you Luv and Akshay...
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Nice information even after 3 years
Thanks 🤗 please share these videos and help me grow this channel
Thank you, sirs, I have DLP Analyst interview tomorrow! 💪🤞
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this was really awesome information !!!
@11:00 We can use EFT(Enterprise File Transfer) as an alternate file transfer mechanism if USB ports are blocked. EFT has a profile bases transfer mechanism like email and support heavy files whereas Email can have file size restrictions varying organization to organization.
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Hi luv,
It was really a great session, very knowledge.
I'm from IT background and I want to enter into DLP as my organization is hiring for DLP team. Please share the plate from where I can start.
Hii team,
Can you pls tell me that if go the customer and tell that we classify data so what are the parameters we ask to customer
Does Anzentech provide technical training for students?
I provide, please whatsapp me on +91 971 860 3114
Can you just answer whats the exact difference of using a proofpoint casb + dlp and symantec dlp and also microsoft purview dlp?
Certainly! Here's a comparison of Proofpoint CASB + DLP, Symantec DLP, and Microsoft Purview DLP, focusing on their key differences:
Proofpoint CASB + DLP
Proofpoint CASB (Cloud Access Security Broker):
Cloud Security: Monitors and controls the use of cloud applications, providing visibility into cloud activity and enforcing security policies.
Threat Protection: Identifies and mitigates threats within cloud environments, such as malware and compromised accounts.
Compliance: Ensures compliance with industry standards by enforcing policies and providing audit trails.
Proofpoint DLP (Data Loss Prevention):
Content Inspection: Analyzes content to prevent sensitive data from leaving the organization.
Policy Enforcement: Applies policies to control data movement based on content and context.
Integration: Seamless integration with Proofpoint’s email and cloud security solutions.
Symantec DLP
Symantec DLP (Data Loss Prevention):
Comprehensive Coverage: Protects sensitive data across endpoints, networks, storage, and cloud.
Advanced Detection: Uses machine learning and data fingerprinting to accurately identify sensitive data.
Incident Response: Provides detailed incident analysis and automated response capabilities.
Unified Management: Centralized policy management and reporting for all data protection activities.
Integration: Integrates with various Symantec security solutions, including endpoint protection and encryption.
Microsoft Purview DLP
Microsoft Purview DLP (Data Loss Prevention):
Microsoft Ecosystem Integration: Deep integration with Microsoft 365 services (e.g., Exchange, SharePoint, OneDrive, Teams).
Unified Management: Single pane of glass for managing data protection policies across Microsoft services.
Real-time Alerts and Reporting: Real-time monitoring, alerting, and detailed reporting.
Built-in AI and Machine Learning: Utilizes Microsoft’s AI capabilities to enhance data protection and policy enforcement.
Compliance and Regulations: Supports compliance with various industry standards and regulations.
Data Sensitivity Labels: Leverages sensitivity labels to classify and protect data based on predefined or custom policies.
Key Differences
Integration and Ecosystem:
Proofpoint: Best suited for organizations heavily using Proofpoint’s suite for email and cloud security.
Symantec: Provides broad protection across various platforms and integrates well with other Symantec solutions.
Microsoft Purview: Ideal for organizations deeply invested in Microsoft 365, offering seamless integration with Microsoft services.
Detection and Enforcement:
Proofpoint: Emphasizes threat protection in cloud environments and integrates with their existing email and cloud security solutions.
Symantec: Strong in advanced detection techniques such as data fingerprinting and machine learning.
Microsoft Purview: Leverages Microsoft’s AI and machine learning capabilities for enhanced data classification and protection.
Deployment and Management:
Proofpoint: Offers both cloud and on-premises deployment options, managed through Proofpoint’s interface.
Symantec: Typically requires more complex deployment, but offers comprehensive management through a centralized console.
Microsoft Purview: Managed within the Microsoft 365 Compliance Center, providing a unified management experience for Microsoft users.
Compliance and Reporting:
Proofpoint: Provides detailed compliance and audit trails, particularly within cloud environments.
Symantec: Known for robust incident response and detailed reporting capabilities.
Microsoft Purview: Offers real-time monitoring and compliance reporting, with a focus on integration within Microsoft 365.
@@LearnITSecuritywithLuvJohar Thank you.
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@@LearnITSecuritywithLuvJohar yup
Dlp agent works properly without vpn ?
Data Loss Prevention (DLP) agents are designed to monitor and control data flows within an organization's network or on endpoints, irrespective of whether a Virtual Private Network (VPN) is in use. However, the effectiveness and scope of DLP agent functionality can vary based on how it is configured and deployed.
Here are a few key points to consider regarding DLP agents and VPN usage:
DLP Functionality Without VPN:
DLP agents can function without a VPN, primarily focusing on data protection and monitoring within the organization's local network or on individual endpoints.
These agents can monitor and enforce data protection policies for sensitive data at rest, in transit (e.g., email or file transfers), and in use (e.g., within applications).
DLP agents typically monitor data on endpoints, network traffic, and communication channels to detect and prevent unauthorized data transfers or breaches.
VPN and Remote Work:
With the rise of remote work, many organizations deploy DLP agents on remote endpoints to maintain data security and compliance. DLP agents can monitor and control data on devices connected to the internet, including when remote employees use VPNs to access company resources.
DLP and VPN Integration:
Some organizations integrate DLP solutions with VPNs to provide data protection for remote users. This integration ensures that DLP policies are consistently enforced for users regardless of their location and connection method.
Challenges with VPN:
VPNs can sometimes pose challenges for DLP because they encrypt network traffic. When data is encrypted within the VPN tunnel, DLP agents may have limited visibility into the actual content of the data, making content inspection and policy enforcement more challenging.
Considerations for DLP Deployment:
The effectiveness of DLP agents depends on how they are configured, where they are deployed, and the policies that are set up. To maximize the effectiveness of DLP in a VPN environment, organizations may need to consider endpoint DLP solutions, content inspection, and integration with VPNs, if applicable.
In summary, DLP agents are designed to work within an organization's network and on endpoints to monitor, detect, and prevent data loss. They can function independently of VPN usage, but their effectiveness in a VPN environment may require careful configuration and integration, particularly when dealing with encrypted data. Organizations should tailor their DLP strategy to their specific needs, including support for remote work and VPN usage if applicable.
I wanted to be added to what's app group,could you please share the link
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How can we legitimate the given number is credit card number
Check Length:
Most credit card numbers have a fixed length, typically 13 to 19 digits.
American Express cards have 15 digits, while Visa, MasterCard, and Discover cards usually have 16 digits.
Issuer Identification Number (IIN) or Bank Identification Number (BIN):
The first few digits of a credit card number represent the issuer or bank.
Visa numbers start with 4, MasterCard with 5, American Express with 3, and Discover with 6.
Thanks for sharing the information. How to join your whatsapp grp please?
chat.whatsapp.com/B9gnx6t68ul5jAWUix7wwl
This what's app group link not working could you please share any new link
If possible to block mobile device change mod as Android /apple
Charging mod
How to reduce false positive
Select DLP incidents data of atleast a week and then import in CSV after you can analyse where you are getting false positives (you need to determine if you are getting much incidents for some particular destinations or for any particular person etc.), after you can decide the action accordingly.
thanks, please keep watching and share if you like this video :)
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