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Use Redshift Data Scrambling for Additional Data Protection

May 3, 2023
8
Min Read
Author Image
Veronica Marinov
Data Security Researcher
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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 Masking

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. 

Redshift create cluster

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.

Amazon CloudWatch

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.

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Author Image
Veronica Marinov
Data Security Researcher
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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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Hybrid Environments: Expand DSPM with On-Premises Scanners

Hybrid Environments: Expand DSPM with On-Premises Scanners

Data Security Posture Management (DSPM) has quickly become a must-have for organizations moving to the cloud. By discovering, classifying, and protecting sensitive data across SaaS apps and cloud services, DSPM gave security teams visibility into data risks they never knew they had before.

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But here’s the reality: most enterprises aren’t 100% cloud. Legacy file shares, private databases, and hybrid workloads still hold massive amounts of sensitive data. Without visibility into these environments, even the most advanced DSPM platforms leave critical blind spots.

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That’s why DSPM platform support is evolving - from cloud-only to truly hybrid.

The Evolution of DSPM

DSPM emerged as a response to the visibility problem created by rapid cloud adoption. As organizations moved to cloud services, SaaS applications, and collaboration platforms, sensitive data began to sprawl across environments at a pace traditional security tools couldn’t keep up with. Security teams suddenly faced oversharing, inconsistent access controls, and little clarity on where critical information actually lived.

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DSPM helped fill this gap by delivering a new level of insight into cloud data. It allowed organizations to map sensitive information across their environments, highlight risky exposures, and begin enforcing least-privilege principles at scale. For cloud-native companies, this represented a huge leap forward - finally, there was a way to keep up with constant data changes and movements, helping customers safely adopt the cloud while maintaining data security best practices and compliance and without slowing innovation.

But for large enterprises, the model was incomplete. Decades of IT infrastructure meant that vast amounts of sensitive information still lived in legacy databases, file shares, and private cloud environments. While DSPM gave them visibility in the cloud, it left everything else in the dark.

The Blind Spot of On-Prem & Private Data

Despite rapid cloud adoption and digital transformation progress, large organizations still rely heavily on hybrid and on-prem environments, since data movement to the cloud can be a year’s long process. On-premises file shares such as NetApp ONTAP, SMB, and NTFS, alongside enterprise databases like Oracle, SQL Server, and MySQL, remain central to operations. Private cloud applications are especially common in regulated industries like healthcare, finance, and government, where compliance demands keep critical data on-premises.

To scan on premises data, many DSPM providers offer partial solutions by taking ephemeral ‘snapshots’ of that data and temporarily moving it to the cloud (either within customer environment, as Sentra does, or to the vendor cloud as some others do) for classification analysis. This can satisfy some requirements, but often is seen as a compliance risk for very sensitive or private data which must remain on-premises. What’s left are two untenable alternatives - ignoring the data which leaves serious visibility gaps or utilizing manual techniques which do not scale.

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These approaches were clearly not built for today’s security or operational requirements. Sensitive data is created and proliferates rapidly, which means it may be unclassified, unmonitored, and overexposed, but how do you even know? From a compliance and risk standpoint, DSPM without on-prem visibility is like watching only half the field, and leaving the other half open to attackers or accidental exposure.

Expanding with On-Prem Scanners

Sentra is changing the equation. With the launch of its on-premise scanners, the platform now extends beyond the cloud to hybrid and private environments, giving organizations a single pane of glass for all their data security.

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With Sentra, organizations can:

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  • Discover and classify sensitive data across traditional file shares (SMB, NFS, CIFS, NTFS) and enterprise databases (Oracle, SQL Server, MySQL, MSSQL, PostgreSDL, MongoDB, MariaDB, IBM DB2, Teradata).
  • Detects and protects critical data as it moves between on-prem and cloud environments.
  • Apply AI-powered classification and enforce Microsoft Purview labeling consistently across environments.
  • Strengthen compliance with frameworks that demand full visibility across hybrid estates.
  • Have a choice of deployment models that best fits their security, compliance, and operational requirements.

Crucially, Sentra’s architecture allows customers to ensure private data always remains in their own environment. They need not move data outside their premises and nothing is ever copied into Sentra’s cloud, making it a trusted choice for enterprises that require secure, private data processing.

Real-World Impact

Picture a global bank: with modern customer-facing websites and mobile applications hosted in the public cloud, providing agility and scalability for digital services. At the same time, the bank continues to rely on decades-old operational databases running in its private cloud — systems that power core banking functions such as transactions and account management. Without visibility into both, security teams can’t fully understand the risks these stores may pose and enforce least privilege, prevent oversharing, or ensure compliance.

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With hybrid DSPM powered by on-prem scanners, that same bank can unify classification and governance across every environment - cloud or on-prem, and close the gaps that attackers or AI systems could otherwise exploit.

Conclusion

DSPM solved the cloud problem. But enterprises aren’t just in the cloud, they’re hybrid. Legacy systems and private environments still hold critical data, and leaving them out of your security posture is no longer an option.

Sentra’s on-premise scanners mark the next stage of DSPM evolution: one unified platform for cloud, on-prem, and private environments. With full visibility, accurate classification, and consistent governance, enterprises finally have the end-to-end data security they need for the AI era.

Because protecting half your data is no longer enough.

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In today’s data-driven world, organizations are dealing with more information than ever before. Data pours in from countless production systems and applications, and data analysts are tasked with making sense of it all - fast. To extract valuable insights, teams rely on powerful analytics platforms like Snowflake, Databricks, BigQuery, and Redshift. These tools make it easier to store, process, and analyze data at scale.

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But while these platforms are excellent at managing raw data, they don't solve one of the most critical challenges organizations face: understanding and securing that data.

That’s where metadata catalogs come in.

Metadata Catalogs Are Essential But They’re Not Enough

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This organizational visibility is essential. With a good catalog in place, teams can collaborate more efficiently, minimize redundancy, and boost productivity by making data discoverable and accessible.

But while these tools are great for discovery, they fall short in one key area: security. They aren’t built to detect risky permissions, identify regulated data, or prevent unintended exposure. And in an era of growing privacy regulations and data breach threats, that’s a serious limitation.

Different Data Tools, Different Gaps

It’s also important to recognize that not all tools in the data stack work the same way. For example, platforms like Snowflake and BigQuery come with fully managed infrastructure, offering seamless integration between storage, compute, and analytics. Others, like Databricks or Redshift, are often layered on top of external cloud storage services like S3 or ADLS, providing more flexibility but also more complexity.

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Metadata tools have similar divides. AWS Glue is tightly integrated into the AWS ecosystem, while tools like Apache Iceberg and Hive Metastore are open and cloud-agnostic, making them suitable for diverse lakehouse architectures.

This variety introduces fragmentation, and with fragmentation comes risk. Inconsistent access policies, blind spots in data discovery, and siloed oversight can all contribute to security vulnerabilities.

The Blind Spots Metadata Can’t See

Even with a well-maintained catalog, organizations can still find themselves exposed. Metadata tells you what data exists, but it doesn’t reveal when sensitive information slips into the wrong place or becomes overexposed.

This problem is particularly severe in analytics environments. Unlike production environments, where permissions are strictly controlled, or SaaS applications, which have clear ownership and structured access models, data lakes and warehouses function differently. They are designed to collect as much information as possible, allowing analysts to freely explore and query it.

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In practice, this means data often flows in without a clear owner and frequently without strict permissions. Anyone with warehouse access, whether users or automated processes, can add information, and analysts typically have broad query rights across all data. This results in a permissive, loosely governed environment where sensitive data such as PII, financial records, or confidential business information can silently accumulate. Once present, it can be accessed by far more individuals than appropriate.

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The good news is that the remediation process doesn't require a heavy-handed approach. Often, it's not about managing complex permission models or building elaborate remediation workflows. The crucial step is the ability to continuously identify and locate sensitive data, understand its location, and then take the correct action whether that involves removal, masking, or locking it down.

How Sentra Bridges the Gap Between Data Visibility & Security

This is where Sentra comes in.

Sentra’s Data Security Posture Management (DSPM) platform is designed to complement and extend the capabilities of metadata catalogs, not just to address their limitations, but to elevate your entire data security strategy. Instead of replacing your metadata layer, Sentra works alongside it enhancing your visibility with real-time insights and powerful security controls.

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Sentra scans across modern data platforms like Snowflake, S3, BigQuery, and more. It automatically classifies and tags sensitive data, identifies potential exposure risks, and detects compliance violations as they happen.

With Sentra, your metadata becomes actionable.

sentra dashboard datasets

From Static Maps to Live GPS

Think of your metadata catalog as a map. It shows you what’s out there and how things are connected. But a map is static. It doesn’t tell you when there’s a roadblock, a detour, or a collision. Sentra transforms that map into a live GPS. It alerts you in real time, enforces the rules of the road, and helps you navigate safely no matter how fast your data environment is moving.

Conclusion: Visibility Without Security Is a Risk You Can’t Afford

Metadata catalogs are indispensable for organizing data at scale. But visibility alone doesn’t stop a breach. It doesn’t prevent sensitive data from slipping into the wrong place, or from being accessed by the wrong people.

To truly safeguard your business, you need more than a map of your data—you need a system that continuously detects, classifies, and secures it in real time. Without this, you’re leaving blind spots wide open for attackers, compliance violations, and costly exposure.

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Sentra turns static visibility into active defense. With real-time discovery, context-rich classification, and automated protection, it gives you the confidence to not only see your data, but to secure it.

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See clearly. Understand fully. Protect confidently with Sentra.

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Sentra Achieves TX-RAMP Certification: Demonstrating Leadership in Data Security Compliance

Introduction

We’re excited to announce that Sentra has officially achieved TX-RAMP certification, a significant milestone that underscores our commitment to delivering trusted, compliant, and secure cloud data protection.

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The Texas Risk and Authorization Management Program (TX-RAMP) establishes rigorous security standards for cloud products and services used by Texas state agencies. Achieving this certification validates that Sentra meets and exceeds these standards, ensuring our customers can confidently rely on our platform to safeguard sensitive data.

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For agencies and organizations operating in Texas, this means streamlined procurement, faster adoption, and the assurance that Sentra’s solutions are fully aligned with state-mandated compliance requirements. For our broader customer base, TX-RAMP certification reinforces Sentra’s role as a trusted leader in data security posture management (DSPM) and our ongoing dedication to protecting data everywhere it lives.

What is TX-RAMP?

The Texas Risk and Authorization Management Program (TX-RAMP) is the state’s framework for evaluating the security of cloud solutions used by public sector agencies. Its goal is to ensure that organizations working with Texas state data meet strict standards for risk management, compliance, and operational security.

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TX-RAMP certification focuses on key areas such as:

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  • Audit & Accountability: Ensuring system activity is monitored, logged, and reviewed.
  • System Integrity: Protecting against malicious code and emerging threats.
  • Access Control: Managing user accounts and privileges with least-privilege principles.
  • Policy & Governance: Establishing strong security policies and updating them regularly.

By certifying vendors, TX-RAMP helps agencies reduce risk, streamline procurement, and ensure sensitive state and citizen data is well protected.

Why TX-RAMP Certification Matters

For Texas agencies, TX-RAMP certification means trust and speed. Working with a certified partner like Sentra simplifies procurement, reduces onboarding time, and provides confidence that solutions meet the state’s toughest security requirements.

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For enterprises and organizations outside Texas, this milestone is just as meaningful. TX-RAMP certification validates that Sentra’s DSPM platform can meet and go beyond some of the most demanding compliance frameworks in the U.S. It’s another proof point that when customers choose Sentra, they are choosing a solution built with security, accountability, and transparency at its core.

Sentra’s Path to TX-RAMP Certification

Achieving TX-RAMP certification required proving that Sentra’s security controls align with strict state requirements.

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Some of the measures that demonstrate compliance include:

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  • Audit and Accountability: Continuous monitoring and quarterly reviews of audit logs under SOC 2 Type II governance.
  • System and Information Integrity: Endpoint protection and weekly scans to prevent, detect, and respond to malicious code.
  • Access Control: Strong account management practices using Okta, BambooHR, MFA, and quarterly access reviews.
  • Change Management and Governance: Structured SDLC processes with documented requests, multi-level approvals, and complete audit trails.

Together, these safeguards show that Sentra doesn’t just comply with TX-RAMP - we exceed the requirements, embedding security into every layer of our operations and platform.

What This Means for Sentra Customers

For Texas agencies, TX-RAMP certification makes it easier and faster to adopt Sentra’s platform, knowing that it has already been vetted against the state’s most stringent standards.

For global enterprises, it’s another layer of assurance: Sentra’s DSPM solution is designed to stand up to the highest levels of compliance practice, giving customers confidence that their most sensitive data is secure - wherever it lives.

Conclusion

Earning TX-RAMP certification is a major milestone in Sentra’s journey, but it’s only part of our broader mission: building trust through security, compliance, and innovation.

‍

This recognition reinforces Sentra’s role as a leader in data security posture management (DSPM) and gives both public sector and private enterprises confidence that their data is safeguarded by a platform designed for the most demanding environments.

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