Overview

Microsoft 365 Information Protection (MIP) provides a comprehensive set of policies to protect your organization’s sensitive data. These policies can be applied to a variety of data sources, including:

Exchange Online
SharePoint Online
OneDrive for Business
Microsoft Teams
Yammer

MIP policies can be used to:

Classify data: Identify and classify sensitive data based on its content.
Protect data: Encrypt or watermark sensitive data to prevent unauthorized access.
Control access: Restrict access to sensitive data to authorized users only.
Monitor data: Track and audit access to sensitive data.

Benefits of Using MIP Policies

There are many benefits to using MIP policies, including:

Improved data security: MIP policies help to protect your organization’s sensitive data from unauthorized access, use, or disclosure.
Reduced risk of data breaches: MIP policies can help to reduce the risk of data breaches by identifying and protecting sensitive data.
Improved compliance: MIP policies can help your organization to comply with data protection regulations such as GDPR and HIPAA.
Increased productivity: MIP policies can help to increase productivity by automating the classification and protection of sensitive data.

How to Create MIP Policies

Creating MIP policies is a straightforward process. You can create policies in the Microsoft 365 Security & Compliance Center.

To create a MIP policy, follow these steps:

In the Security & Compliance Center, click Information protection.
Click Policies.
Click Create policy.
Enter a name and description for the policy.
Select the data sources that you want to apply the policy to.
Configure the policy settings.
Click Create.

Types of MIP Policies

There are three types of MIP policies:

Classification policies: Classify data based on its content.
Protection policies: Encrypt or watermark sensitive data to prevent unauthorized access.
Access control policies: Restrict access to sensitive data to authorized users only.

MIP Policy Settings

The following table describes the settings that you can configure for MIP policies:

Setting	Description
Name	The name of the policy.
Description	A description of the policy.
Data sources	The data sources that the policy applies to.
Classification rules	The rules that are used to classify data.
Protection rules	The rules that are used to protect data.
Access control rules	The rules that are used to control access to data.
Monitoring settings	The settings that are used to track and audit access to data.

Best Practices for Using MIP Policies

Here are some best practices for using MIP policies:

Start with a small number of policies. Don’t try to create too many policies at once. Start with a few simple policies and add more as needed.
Use clear and concise policy names. This will make it easier to manage and find policies.
Test your policies. Before you deploy a policy, test it to make sure that it works as expected.
Monitor your policies. Regularly review the logs to see how your policies are working. Make adjustments as needed.

Frequently Asked Questions (FAQ)

What is the difference between a classification policy and a protection policy?

A classification policy identifies and classifies sensitive data. A protection policy encrypts or watermarks sensitive data to prevent unauthorized access.

What is the difference between access control and data loss prevention (DLP)?

Access control restricts access to sensitive data to authorized users only. DLP prevents sensitive data from being shared with unauthorized users.

How can I get started with MIP policies?

You can create MIP policies in the Microsoft 365 Security & Compliance Center. For more information, see the Microsoft documentation.

Intel® Confidential Computing

Intel® Confidential Computing provides hardware-based security features that protect data in use, improving data privacy and security.

Key Features:

Intel® Software Guard Extensions (SGX): Creates isolated memory enclaves that protect code and data from unauthorized access.
Intel® Trusted Execution Environment (TXE): Creates a secure environment for hardware initialization, firmware loading, and boot processes.
Intel® Platform Trust Technology (PTT): Stores cryptographic keys and other sensitive information securely in hardware.

Benefits:

Enhanced data privacy: Data can be processed in a confidential environment, reducing the risk of exposure.
Improved security: Confidential computing prevents malicious code and unauthorized parties from accessing sensitive data.
Regulatory compliance: Meets industry regulations requiring data protection, such as GDPR and PCI DSS.
Trustworthy computing: Hardware-based security features provide a verified and auditable root of trust.

Applications:

Cloud computing: Protects sensitive data stored or processed in the cloud.
Data analytics: Encrypts data during analysis, preserving privacy while providing insights.
Financial services: Secures financial transactions and customer data.
Healthcare: Protects patient information and medical records.

AMD Memory Encryption

AMD Memory Encryption is a hardware-based security feature that encrypts data stored in the computer’s memory. This helps protect the data from unauthorized access, even if the computer is stolen or hacked.

AMD Memory Encryption is available on select AMD Ryzen processors. It is enabled by default on supported systems. The encryption key is stored in the processor’s security chip, which is tamper-resistant.

Memory encryption can slow down the computer’s performance slightly. However, the security benefits of memory encryption outweigh the performance impact for most users. If you are concerned about the performance impact, you can disable memory encryption in the BIOS settings.

Benefits of AMD Memory Encryption:

Protects data from unauthorized access
Helps prevent data breaches
Complies with data protection regulations
Easy to enable and manage

Data Classification and Sensitivity Analysis

Data classification involves identifying and organizing data based on its importance, confidentiality, and potential impact if compromised. By categorizing data, organizations can establish appropriate security measures to protect the most sensitive information.

Sensitivity analysis, on the other hand, evaluates the impact of uncertain input parameters on the output of a model or simulation. By varying the input values within a defined range, organizations can assess the potential risks and vulnerabilities associated with different scenarios. Together, data classification and sensitivity analysis provide a comprehensive approach to data security and risk management.

Information Sensitivity Policies in the Cloud

Information sensitivity policies are critical for protecting sensitive data in the cloud. These policies allow organizations to classify data based on its sensitivity level and implement appropriate security measures to protect it. By understanding and implementing effective information sensitivity policies, organizations can reduce the risk of data breaches, improve compliance, and maintain the confidentiality, integrity, and availability of their sensitive data.

Benefits of Information Sensitivity Policies in the Cloud:

Enhanced data protection: Identify and protect sensitive data by classifying it based on its sensitivity level and implementing appropriate security controls.
Improved compliance: Meet regulatory and industry standards by adhering to data protection requirements for sensitive information.
Reduced risk of data breaches: Prevent unauthorized access and exfiltration of sensitive data by implementing access controls and encryption measures.
Increased efficiency: Automate data classification and protection processes to reduce manual effort and improve accuracy.
Enhanced visibility: Gain visibility into sensitive data usage and access to identify potential security risks and take appropriate actions.

Microsoft Azure Information Protection

Microsoft Azure Information Protection (AIP) is a cloud-based information protection solution that helps organizations to protect sensitive data from unauthorized access and misuse. AIP provides a comprehensive set of features that enable organizations to:

Classify and label data based on its sensitivity
Encrypt data at rest and in transit
Control access to data based on user roles and permissions
Track and audit data access
Protect data from malware and ransomware

AIP is a scalable solution that can be deployed to organizations of all sizes. It is easy to use and manage, and it can be integrated with a variety of other Microsoft products and services.

AIP is a valuable tool for organizations that are looking to protect their sensitive data from unauthorized access and misuse. It provides a comprehensive set of features that can help organizations to comply with data protection regulations and industry best practices.

Intel SGX Performance

Intel Software Guard Extensions (SGX) provides enhanced security for enclave-based applications by creating isolated memory regions protected from privileged software. However, this security comes at a performance cost.

Factors Affecting SGX Performance:

Enclave Size: Larger enclaves require more memory, leading to higher memory access latency.
Enclave Operations: Encryption, decryption, and attestation operations performed within enclaves introduce significant overhead.
Enclave Transitions: Switching between enclave and non-enclave code can cause high performance penalties due to caching and memory management requirements.

Performance Optimization Techniques:

Code Optimization: Optimizing code for Intel SGX can reduce overhead by reducing enclave size and minimizing enclave operations.
Hardware Acceleration: SGX hardware assists can offload cryptographic operations, improving enclave performance.
Enclave Isolation: Separate enclaves for different tasks can minimize enclave transitions and overhead.
Asymmetric Encryption: Using asymmetric encryption instead of symmetric encryption for external communication can reduce enclave transition costs.
Lookaside Buffers: Memory buffers can be used to cache frequently accessed data, reducing latency within enclaves.

Quantifying SGX Performance:

Benchmarking tools like SGXBench can provide quantitative performance metrics for SGX applications, allowing developers to identify and address bottlenecks.

Trade-offs:

The performance impact of SGX must be weighed against the enhanced security it provides. For applications where security is paramount and performance can be sacrificed to some extent, SGX remains a valuable technology.

AMD SEV Benchmarks

Performance Impact:

SEV overhead varies with workload and configuration.
Encryption/decryption overhead can reduce performance by 5-10%.
Nested virtualization (NV) and SEV can have a combined performance impact of up to 15%.

Security Enhancements:

SEV significantly improves system security by isolating virtual machines (VMs) from the host.
Data is encrypted at rest and during transit, reducing the risk of data breaches.
Protected memory regions (PMRs) provide additional protection against memory attacks.

Use Cases:

Confidential computing: Protecting sensitive data in cloud and enterprise environments.
Cryptographic operations: Securing key generation, storage, and usage within VMs.
Privacy-sensitive workloads: Isolating personal or medical data from unauthorized access.

Data Protection Best Practices for Sensitive Information

Confidentiality:

Implement access controls to restrict access to sensitive data to authorized individuals.
Use encryption to protect data in transit and at rest.
Avoid storing sensitive information in public locations, such as cloud storage without proper authentication.

Integrity:

Regularly monitor data for unauthorized changes or breaches.
Use checksums or hash functions to ensure data accuracy.
Implement mechanisms to prevent alteration or corruption of data.

Availability:

Ensure that sensitive data is backed up and stored in a secure location.
Implement disaster recovery plans to restore data in case of a system failure or loss.
Regularly test data backup and recovery procedures.

Minimization:

Collect only the necessary sensitive data and delete it when it is no longer required.
Avoid storing sensitive data in multiple locations or systems.
De-identify or pseudonymize sensitive data whenever possible.

Pseudonymization/Anonymization:

Replace sensitive data with pseudonyms or anonymized values to protect individuals’ privacy.
Ensure that pseudonymization or anonymization methods are irreversible to prevent re-identification.

Data Disposal:

Implement secure data disposal procedures to prevent data from falling into unauthorized hands.
Physically destroy media containing sensitive data or securely erase electronic files.
Regularly review data retention policies to ensure sensitive data is not stored indefinitely.

Security Awareness and Training:

Educate employees on data protection best practices and the risks of data breaches.
Provide regular training on data handling procedures, access controls, and reporting vulnerabilities.
Train employees to identify and respond to phishing attacks or other attempts to access sensitive data.

Risk Assessment and Information Sensitivity

Understanding information sensitivity is crucial for conducting effective risk assessments. The sensitivity level of information (e.g., classified, sensitive but unclassified, public) determines its susceptibility to unauthorized access or disclosure. By identifying and categorizing information based on its sensitivity, organizations can tailor risk assessment processes to prioritize threats and mitigate vulnerabilities.

Key Considerations:

Confidentiality: How sensitive or protected is the information?
Integrity: Can the information be altered or modified without detection?
Availability: Can the information be accessed and used as needed?

Impact of Sensitivity on Risk Assessment:

Impacts the severity of potential threats
Influences the likelihood of successful attacks
Determines the appropriate countermeasures and controls
Guides the development of incident response plans

By incorporating information sensitivity into risk assessments, organizations can allocate resources effectively, prioritize mitigation strategies, and enhance their cybersecurity posture.

Information Leakage Prevention Systems for Sensitive Information

Information Leakage Prevention (ILP) systems aim to detect and prevent the unauthorized disclosure of sensitive information from within an organization. These systems monitor network traffic, analyze content, and implement policies to control data transfer. ILP systems can be deployed as on-premises appliances or cloud-based services.

Key Components:

Network Traffic Monitoring: Monitors all network traffic to identify potential data leaks.
Content Inspection: Scans data for sensitive information, such as personally identifiable information (PII), financial data, and trade secrets.
Policy Enforcement: Implements policies that define what types of sensitive data can be transmitted and to whom.

Benefits:

Data Loss Prevention: Prevents the inadvertent or intentional disclosure of sensitive information.
Compliance: Helps organizations meet regulatory requirements related to data protection.
Reputation Protection: Prevents reputational damage caused by data breaches.