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Use Redshift Data Scrambling for Additional Data Protection

May 3, 2023

Min Read

Veronica Marinov

Data Security Researcher

According to IBM, a data breach in the United States cost companies an average of 9.44 million dollars in 2022. It is now more important than ever for organizations to place high importance on protecting confidential information. Data scrambling, which can add an extra layer of security to data, is one approach to accomplish this.

In this post, we'll analyze the value of data protection, look at the potential financial consequences of data breaches, and talk about how Redshift Data Scrambling may help protect private information.

The Importance of Data Protection

Data protection is essential to safeguard sensitive data from unauthorized access. Identity theft, financial fraud,and other serious consequences are all possible as a result of a data breach. Data protection is also crucial for compliance reasons. Sensitive data must be protected by law in several sectors, including government, banking, and healthcare. Heavy fines, legal problems, and business loss may result from failure to abide by these regulations.

Hackers employ many techniques, including phishing, malware, insider threats, and hacking, to get access to confidential information. For example, a phishing assault may lead to the theft of login information, and malware may infect a system, opening the door for additional attacks and data theft.

So how to protect yourself against these attacks and minimize your data attack surface?

What is Redshift Data Masking?

Redshift data masking is a technique used to protect sensitive data in Amazon Redshift; a cloud-based data warehousing and analytics service. Redshift data masking involves replacing sensitive data with fictitious, realistic values to protect it from unauthorized access or exposure. It is possible to enhance data security by utilizing Redshift data masking in conjunction with other security measures, such as access control and encryption, in order to create a comprehensive data protection plan.

What is Redshift Data Scrambling?

Redshift data scrambling protects confidential information in a Redshift database by altering original data values using algorithms or formulas, creating unrecognizable data sets. This method is beneficial when sharing sensitive data with third parties or using it for testing, development, or analysis, ensuring privacy and security while enhancing usability.

The technique is highly customizable, allowing organizations to select the desired level of protection while maintaining data usability. Redshift data scrambling is cost-effective, requiring no additional hardware or software investments, providing an attractive, low-cost solution for organizations aiming to improve cloud data security.

Data Masking vs. Data Scrambling

Data masking involves replacing sensitive data with a fictitious but realistic value. However, data scrambling, on the other hand, involves changing the original data values using an algorithm or a formula to generate a new set of values.

In some cases, data scrambling can be used as part of data masking techniques. For instance, sensitive data such as credit card numbers can be scrambled before being masked to enhance data protection further.

Setting up Redshift Data Scrambling

Having gained an understanding of Redshift and data scrambling, we can now proceed to learn how to set it up for implementation. Enabling data scrambling in Redshift requires several steps.

To achieve data scrambling in Redshift, SQL queries are utilized to invoke built-in or user-defined functions. These functions utilize a blend of cryptographic techniques and randomization to scramble the data.

The following steps are explained using an example code just for a better understanding of how to set it up:

Step 1: Create a new Redshift cluster

Create a new Redshift cluster or use an existing cluster if available.

Step 2: Define a scrambling key

Define a scrambling key that will be used to scramble the sensitive data.

 
SET session my_scrambling_key = 'MyScramblingKey';

In this code snippet, we are defining a scrambling key by setting a session-level parameter named <inlineCode>my_scrambling_key<inlineCode> to the value <inlineCode>MyScramblingKey<inlineCode>. This key will be used by the user-defined function to scramble the sensitive data.

Step 3: Create a user-defined function (UDF)

Create a user-defined function in Redshift that will be used to scramble the sensitive data.


CREATE FUNCTION scramble(input_string VARCHAR)
RETURNS VARCHAR
STABLE
AS $$
DECLARE
scramble_key VARCHAR := 'MyScramblingKey';
BEGIN
-- Scramble the input string using the key
-- and return the scrambled output
RETURN ;
END;
$$ LANGUAGE plpgsql;

Here, we are creating a UDF named <inlineCode>scramble<inlineCode> that takes a string input and returns the scrambled output. The function is defined as <inlineCode>STABLE<inlineCode>, which means that it will always return the same result for the same input, which is important for data scrambling. You will need to input your own scrambling logic.

Step 4: Apply the UDF to sensitive columns

Apply the UDF to the sensitive columns in the database that need to be scrambled.


UPDATE employee SET ssn = scramble(ssn);

For example, applying the <inlineCode>scramble<inlineCode> UDF to a column saying, <inlineCode>ssn<inlineCode> in a table named <inlineCode>employee<inlineCode>. The <inlineCode>UPDATE<inlineCode> statement calls the <inlineCode>scramble<inlineCode> UDF and updates the values in the <inlineCode>ssn<inlineCode> column with the scrambled values.

Step 5: Test and validate the scrambled data

Test and validate the scrambled data to ensure that it is unreadable and unusable by unauthorized parties.


SELECT ssn, scramble(ssn) AS scrambled_ssn
FROM employee;

In this snippet, we are running a <inlineCode>SELECT<inlineCode> statement to retrieve the <inlineCode>ssn<inlineCode> column and the corresponding scrambled value using the <inlineCode>scramble<inlineCode> UDF. We can compare the original and scrambled values to ensure that the scrambling is working as expected.

Step 6: Monitor and maintain the scrambled data

To monitor and maintain the scrambled data, we can regularly check the sensitive columns to ensure that they are still rearranged and that there are no vulnerabilities or breaches. We should also maintain the scrambling key and UDF to ensure that they are up-to-date and effective.

Different Options for Scrambling Data in Redshift

Selecting a data scrambling technique involves balancing security levels, data sensitivity, and application requirements. Various general algorithms exist, each with unique pros and cons. To scramble data in Amazon Redshift, you can use the following Python code samples in conjunction with a library like psycopg2 to interact with your Redshift cluster. Before executing the code samples, you will need to install the psycopg2 library:


pip install psycopg2

Random

Utilizing a random number generator, the Random option quickly secures data, although its susceptibility to reverse engineering limits its robustness for long-term protection.


import random
import string
import psycopg2

def random_scramble(data):
    scrambled = ""
    for char in data:
        scrambled += random.choice(string.ascii_letters + string.digits)
    return scrambled

# Connect to your Redshift cluster
conn = psycopg2.connect(host='your_host', port='your_port', dbname='your_dbname', user='your_user', password='your_password')
cursor = conn.cursor()
# Fetch data from your table
cursor.execute("SELECT sensitive_column FROM your_table;")
rows = cursor.fetchall()

# Scramble the data
scrambled_rows = [(random_scramble(row[0]),) for row in rows]

# Update the data in the table
cursor.executemany("UPDATE your_table SET sensitive_column = %s WHERE sensitive_column = %s;", [(scrambled, original) for scrambled, original in zip(scrambled_rows, rows)])
conn.commit()

# Close the connection
cursor.close()
conn.close()

Shuffle

The Shuffle option enhances security by rearranging data characters. However, it remains prone to brute-force attacks, despite being harder to reverse-engineer.


import random
import psycopg2

def shuffle_scramble(data):
    data_list = list(data)
    random.shuffle(data_list)
    return ''.join(data_list)

conn = psycopg2.connect(host='your_host', port='your_port', dbname='your_dbname', user='your_user', password='your_password')
cursor = conn.cursor()

cursor.execute("SELECT sensitive_column FROM your_table;")
rows = cursor.fetchall()

scrambled_rows = [(shuffle_scramble(row[0]),) for row in rows]

cursor.executemany("UPDATE your_table SET sensitive_column = %s WHERE sensitive_column = %s;", [(scrambled, original) for scrambled, original in zip(scrambled_rows, rows)])
conn.commit()

cursor.close()
conn.close()

Reversible

By scrambling characters in a decryption key-reversible manner, the Reversible method poses a greater challenge to attackers but is still vulnerable to brute-force attacks. We’ll use the Caesar cipher as an example.


def caesar_cipher(data, key):
    encrypted = ""
    for char in data:
        if char.isalpha():
            shift = key % 26
            if char.islower():
                encrypted += chr((ord(char) - 97 + shift) % 26 + 97)
            else:
                encrypted += chr((ord(char) - 65 + shift) % 26 + 65)
        else:
            encrypted += char
    return encrypted

conn = psycopg2.connect(host='your_host', port='your_port', dbname='your_dbname', user='your_user', password='your_password')
cursor = conn.cursor()

cursor.execute("SELECT sensitive_column FROM your_table;")
rows = cursor.fetchall()

key = 5
encrypted_rows = [(caesar_cipher(row[0], key),) for row in rows]
cursor.executemany("UPDATE your_table SET sensitive_column = %s WHERE sensitive_column = %s;", [(encrypted, original) for encrypted, original in zip(encrypted_rows, rows)])
conn.commit()

cursor.close()
conn.close()

Custom

The Custom option enables users to create tailor-made algorithms to resist specific attack types, potentially offering superior security. However, the development and implementation of custom algorithms demand greater time and expertise.

Best Practices for Using Redshift Data Scrambling

There are several best practices that should be followed when using Redshift Data Scrambling to ensure maximum protection:

Use Unique Keys for Each Table

To ensure that the data is not compromised if one key is compromised, each table should have its own unique key pair. This can be achieved by creating a unique index on the table.


CREATE UNIQUE INDEX idx_unique_key ON table_name (column_name);

Encrypt Sensitive Data Fields

Sensitive data fields such as credit card numbers and social security numbers should be encrypted to provide an additional layer of security. You can encrypt data fields in Redshift using the ENCRYPT function. Here's an example of how to encrypt a credit card number field:


SELECT ENCRYPT('1234-5678-9012-3456', 'your_encryption_key_here');

Use Strong Encryption Algorithms

Strong encryption algorithms such as AES-256 should be used to provide the strongest protection. Redshift supports AES-256 encryption for data at rest and in transit.


CREATE TABLE encrypted_table (  sensitive_data VARCHAR(255) ENCODE ZSTD ENCRYPT 'aes256' KEY 'my_key');

Control Access to Encryption Keys

Access to encryption keys should be restricted to authorized personnel to prevent unauthorized access to sensitive data. You can achieve this by setting up an AWS KMS (Key Management Service) to manage your encryption keys. Here's an example of how to restrict access to an encryption key using KMS in Python:


import boto3

kms = boto3.client('kms')

key_id = 'your_key_id_here'
grantee_principal = 'arn:aws:iam::123456789012:user/jane'

response = kms.create_grant(
    KeyId=key_id,
    GranteePrincipal=grantee_principal,
    Operations=['Decrypt']
)

print(response)

Regularly Rotate Encryption Keys

Regular rotation of encryption keys ensures that any compromised keys do not provide unauthorized access to sensitive data. You can schedule regular key rotation in AWS KMS by setting a key policy that specifies a rotation schedule. Here's an example of how to schedule annual key rotation in KMS using the AWS CLI:

 
aws kms put-key-policy \\
    --key-id your_key_id_here \\
    --policy-name default \\
    --policy
    "{\\"Version\\":\\"2012-10-17\\",\\"Statement\\":[{\\"Effect\\":\\"Allow\\"
    "{\\"Version\\":\\"2012-10-17\\",\\"Statement\\":[{\\"Effect\\":\\"Allow\\"
    \\":\\"kms:RotateKey\\",\\"Resource\\":\\"*\\"},{\\"Effect\\":\\"Allow\\",\
    \"Principal\\":{\\"AWS\\":\\"arn:aws:iam::123456789012:root\\"},\\"Action\\
    ":\\"kms:CreateGrant\\",\\"Resource\\":\\"*\\",\\"Condition\\":{\\"Bool\\":
    {\\"kms:GrantIsForAWSResource\\":\\"true\\"}}}]}"

Turn on logging

To track user access to sensitive data and identify any unwanted access, logging must be enabled. All SQL commands that are executed on your cluster are logged when you activate query logging in Amazon Redshift. This applies to queries that access sensitive data as well as data-scrambling operations. Afterwards, you may examine these logs to look for any strange access patterns or suspect activities.

You may use the following SQL statement to make query logging available in Amazon Redshift:

ALTER DATABASE  SET enable_user_activity_logging=true;

The stl query system table may be used to retrieve the logs once query logging has been enabled. For instance, the SQL query shown below will display all queries that reached a certain table:

Monitor Performance

Data scrambling is often a resource-intensive practice, so it’s good to monitor CPU usage, memory usage, and disk I/O to ensure your cluster isn’t being overloaded. In Redshift, you can use the <inlineCode>svl_query_summary<inlineCode> and <inlineCode>svl_query_report<inlineCode> system views to monitor query performance. You can also use Amazon CloudWatch to monitor metrics such as CPU usage and disk space.

Establishing Backup and Disaster Recovery

In order to prevent data loss in the case of a disaster, backup and disaster recovery mechanisms should be put in place. Automated backups and manual snapshots are only two of the backup and recovery methods offered by Amazon Redshift. Automatic backups are taken once every eight hours by default.

Moreover, you may always manually take a snapshot of your cluster. In the case of a breakdown or disaster, your cluster may be restored using these backups and snapshots. Use this SQL query to manually take a snapshot of your cluster in Amazon Redshift:

CREATE SNAPSHOT ;

To restore a snapshot, you can use the <inlineCode>RESTORE<inlineCode> command. For example:


RESTORE 'snapshot_name' TO 'new_cluster_name';

Frequent Review and Updates

To ensure that data scrambling procedures remain effective and up-to-date with the latest security requirements, it is crucial to consistently review and update them. This process should include examining backup and recovery procedures, encryption techniques, and access controls.

In Amazon Redshift, you can assess access controls by inspecting all roles and their associated permissions in the <inlineCode>pg_roles<inlineCode> system catalog database. It is essential to confirm that only authorized individuals have access to sensitive information.

To analyze encryption techniques, use the <inlineCode>pg_catalog.pg_attribute<inlineCode> system catalog table, which allows you to inspect data types and encryption settings for each column in your tables. Ensure that sensitive data fields are protected with robust encryption methods, such as AES-256.

The AWS CLI commands <inlineCode>aws backup plan<inlineCode> and <inlineCode>aws backup vault<inlineCode> enable you to review your backup plans and vaults, as well as evaluate backup and recovery procedures. Make sure your backup and recovery procedures are properly configured and up-to-date.

Decrypting Data in Redshift

There are different options for decrypting data, depending on the encryption method used and the tools available; the decryption process is similar to of encryption, usually a custom UDF is used to decrypt the data, let’s look at one example of decrypting data scrambling with a substitution cipher.

Step 1: Create a UDF with decryption logic for substitution


CREATE FUNCTION decrypt_substitution(ciphertext varchar) RETURNS varchar
IMMUTABLE AS $$
    alphabet = 'abcdefghijklmnopqrstuvwxyz'
    substitution = 'ijklmnopqrstuvwxyzabcdefgh'
    reverse_substitution = ''.join(sorted(substitution, key=lambda c: substitution.index(c)))
    plaintext = ''
    for i in range(len(ciphertext)):
        index = substitution.find(ciphertext[i])
        if index == -1:
            plaintext += ciphertext[i]
        else:
            plaintext += reverse_substitution[index]
    return plaintext
$$ LANGUAGE plpythonu;

Step 2: Move the data back after truncating and applying the decryption function


TRUNCATE original_table;
INSERT INTO original_table (column1, decrypted_column2, column3)
SELECT column1, decrypt_substitution(encrypted_column2), column3
FROM temp_table;

In this example, encrypted_column2 is the encrypted version of column2 in the temp_table. The decrypt_substitution function is applied to encrypted_column2, and the result is inserted into the decrypted_column2 in the original_table. Make sure to replace column1, column2, and column3 with the appropriate column names, and adjust the INSERT INTO statement accordingly if you have more or fewer columns in your table.

Conclusion

Redshift data scrambling is an effective tool for additional data protection and should be considered as part of an organization's overall data security strategy. In this blog post, we looked into the importance of data protection and how this can be integrated effectively into the data warehouse. Then, we covered the difference between data scrambling and data masking before diving into how one can set up Redshift data scrambling.

Once you begin to accustom to Redshift data scrambling, you can upgrade your security techniques with different techniques for scrambling data and best practices including encryption practices, logging, and performance monitoring. Organizations may improve their data security posture management (DSPM) and reduce the risk of possible breaches by adhering to these recommendations and using an efficient strategy.

‍

Veronica Marinov

Data Security Researcher

Veronica is the security researcher at Sentra. She brings a wealth of knowledge and experience as a cybersecurity researcher. Her main focuses are researching the main cloud provider services and AI infrastructures for Data related threats and techniques.

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But the big question is: Do you know where your Google Workspace data is—and if it’s secure and who has access to it?

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While Google Workspace has become an indispensable pillar in cloud operations and collaboration, its widespread adoption introduces significant security risks that businesses simply can't afford to ignore. To optimize Google Workspace data protection, enterprises must know how Google Workspace protects and classifies data. Knowing the scope, gaps, limitations, and silos of Google Workspace data protection mechanisms can help businesses strategize more effectively to mitigate data risks and ensure more holistic data security coverage across multi-cloud estates.

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Without comprehensive visibility into sensitive data exposures across Google Workspace applications, businesses risk serious security threats. Besides sensitive data exposure, these include exploitable vulnerabilities, external attacks, human error, and shadow data. Complex shared responsibility models and unmet compliance policies also loom large, threatening the security of your data.

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To tackle these risks, businesses must prioritize and optimize data security across Google Workspace products while acknowledging that Google is rarely the sole platform an enterprise uses.

How Does Google Store Your Data?

To understand how to protect sensitive data in Google Workspace, it's essential to first examine how Google stores and manages this data. Why? Because the intricacies of data storage architectures and practices have significant implications for your security posture.

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Here are three-steps to help you understand and optimize your data storage in Google Workspace:

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1. Know Where and How Google Stores Your Data

Google stores your files in customized servers in secure data centers.
Your data is automatically distributed across multiple regions, guaranteeing redundancy and availability.

2. Control Data Retention

Google retains your Workspace data until you or an admin deletes it.
Use Google Vault to manage retention policies and set custom retention rules for emails and files.
Regularly review and clean up unnecessary stored data to reduce security risks.

3. Secure Your Stored Data

Enable encryption for sensitive files in Google Drive.
Restrict who can view, edit, and share stored documents by implementing access controls.
Monitor data access logs to detect unauthorized access.

How Does Google Workspace Classify Your Data?

Google’s built-in classification tools are an acceptable starting point. However, they fall short of securing and classifying all unstructured data across complex cloud environments. This is because today's cloud attack surface expands across multiple providers, making security more complex than ever before. Consequently, Google's myopic classification often snowballs into bigger security problems, as data moves. Because of this evolving attack surface across multi-cloud environments, risk-ridden shadow data and unstructured data fester in Google Workspace apps.

The Issue of Unstructured Data

It’s important to remember that most enterprise data is unstructured. Unstructured data refers to data that isn’t stored in standardized or easily manageable formats. In Google Workspace, this could be data in a Gmail draft, multimedia files in Google Drive, or other informal exchanges of sensitive information between Workspace apps.

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For years, unstructured data has been a nightmare for businesses to map, manage, and secure. Unstructured document stores and employee GDrives are hot zones for data risks. Native Google Drive data classification capabilities can be a useful source of metadata to support a more comprehensive external data classification solution. A cloud-native DSP solution can map, classify, and organize sensitive data, including PHI, PCI, and business secrets, across both Google Workspace and cloud platforms that Google's built-in capabilities do not cover, like AWS and S3.

How Does Google Workspace Protect Your Data?

Like its built-in classification mechanisms, Google's baseline security features, such as encryption and access controls, are good for simple use cases but aren't capable enough to fully protect complex environments.

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For both the classification and security of unstructured data, Google’s native tools may not suffice. A robust data loss prevention (DLP) solution should ideally do the trick for unstructured data. However, Google Workspace DLP alone and other protection measures (formerly referred to as G Suite data protection) are unlikely to provide holistic data security, especially in dynamic cloud environments.

Google Native Tool Challenges

Google’s basic protection measures don't tackle the full spectrum of critical Google Workspace data risks because they can't permeate unstructured documents, where sensitive data may reside in various protected states.

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For example, an employee's personal Google Drive can potentially house exposed and exploitable sensitive data that can slip through Google's built-in security mechanisms. It’s also important to remember that Google Workspace data loss prevention capabilities do nothing to protect critical enterprise data hosted in other cloud platforms.

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Ultimately, while Google provides some security controls, they alone don’t offer the level of protection that today’s complex cloud environments demand. To close these gaps, businesses must look to complement Google’s built-in capabilities and invest in robust data security solutions.

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Only a highly integrable data security tool with advanced AI and ML capabilities can protect unstructured data across Google Workspace’s diverse suite of apps, and further, across the entire enterprise data estate. This has become mandatory since multi-cloud architectures are the norm today.

A Robust Data Security Platform: The Key to Holistic Google Workspace Data Protection

The speed, complexity, and rapid evolution of multi-cloud and hybrid cloud environments demand more advanced data security capabilities than Google Workspace’s native storage, classification, and protection features provide.

It is becoming increasingly difficult to mitigate the risks associated with sensitive data.

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To successfully remediate these risks, businesses urgently need robust data security posture management (DSPM) and data detection and response (DDR) solutions - preferably all in one platform. There's simply no other way to guarantee comprehensive data protection across Google Workspace. Furthermore, as mentioned earlier, most businesses don't exclusively use Google platforms. They often mix and match services from cloud providers like Google, Azure, and AWS.

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In other words, besides limited data classification and protection, Google's built-in capabilities won't be able to extend into other branches of an enterprise's multi-cloud architecture. And having siloed data security tools for each of these cloud platforms increases costs and further complicates administration that can lead to critical coverage gaps. That's why the optimal solution is a holistic platform that can fill the gaps in Google's existing capabilities to provide unified data classification, security, and coverage across all other cloud platforms.

Sentra: The Ultimate Cloud-Agnostic Data Protection and Classification Solution

To truly secure sensitive data across Google Workspace and beyond, enterprises need a cloud-native data security platform. That’s where Sentra comes in. It hands you enterprise-scale data protection by seamlessly integrating powerful capabilities like data discovery and classification, data security posture management (DSPM), data access governance (DAG), and data detection and response (DDR) into an all-in-one, easy-to-use platform.

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By combining rule-based and large language model (LLM)-based classification, Sentra ensures accurate and scalable data security across Workspace apps like Google Drive—as well as data contained in apps from other cloud providers. This is crucial for any enterprise that hosts its data across disparate cloud platforms, not just Workspace. To classify unstructured data across these platforms, Sentra leverages supervised AI training models like BERT. It also uses zero-shot classification techniques to zero in on and accurately classify unstructured data.

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Sentra is particularly useful for anyone asking business-, industry-, or geography-specific data security questions such as “Does Google Workspace have HIPAA compliance frameworks?” and “Is my organization's use of Google Workspace GDPR-compliant?” The short answer to these questions: Integrate Sentra with your Google Workspace apps and you will see.

Boost Your Google Workspace Data Protection with Sentra

By integrating Sentra with Google Workspace, companies can leverage AI-driven insights to distinguish employee data from customer data, ensuring a clearer understanding of their information landscape. Sentra also identifies customer-specific data types, such as personally identifiable information (PII), protected health information (PHI), product IDs, private codes, and localization requirements. Additionally, it detects toxic data combinations that may pose security risks.

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Beyond insights, Sentra provides robust data protection through comprehensive inventorying and classification of unstructured data. It helps organizations right-size permissions, expose shadow data, and implement real-time detection of sensitive data exposure, security breaches, and suspicious activity, ensuring a proactive approach to data security.

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No matter where your unstructured data resides, whether in Google Drive or any other cloud service, Sentra ensures it is accurately identified, classified, and protected with over 95% precision.

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If you’re ready to take control of your data security, book a demo to discover how Sentra’s AI-driven protection secures your most valuable information across Google Workspace and beyond.