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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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Why "Good Enough" DSPM Isn’t Enough Anymore

Change in fintech moves faster than ever. The DSPM for the financial services sector is growing at breakneck speed. But as financial applications get more sophisticated, and with cloud and AI adoption soaring, the old "good enough" DSPM falls short. Sensitive data is everywhere now. 82% percent of breaches happen in the cloud, with 39% stretching across multi-cloud or hybrid setups according to The Future of Data Security: Why DSPM is Here to Stay. Enterprise data is set to exceed 181 zettabytes by 2025, raising the stakes for automation, real-time classification, and tight integration with core infrastructure.

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AI and automation are no longer optional. To effectively reduce risk and keep compliance manageable and truly auditable, DSPM systems need to automate classification, masking, remediation, and reporting as a central part of operations, not as last-minute additions.

Where Most DSPM Solutions Fall Short

Fintech organizations often struggle to scale legacy or early DSPM and DLP products, especially those similar to emerging DSPM or large CNAPP vendors. These tools might offer broad control and AI-powered classification, but they usually require too much manual orchestration to achieve full remediation, only automate certain pieces of the workflow, and rely on separate masking add-ons.

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That leads to gaps in AI and multi-cloud data context, choppy visibility, and much of the workflow stuck in manual gear, a recipe for persistent exposure of sensitive data, especially in fast-moving fintech environments.

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  • Integrated for modern stacks: Ready-made, with out-of-the-box connections for Snowflake, Bedrock, Microsoft 365, and the wider AWS/fintech ecosystem.

More and more fintech companies are switching to Sentra DSPM to achieve true cross-cloud visibility and meet regulations without slowing down. By plugging into fintech data flows and covering AI model pipelines, Sentra lets organizations use DSPM with the same speed as their business.

Building a Future-Ready DSPM Strategy in Financial Services

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Next-gen DSPM means: less busywork, no more juggling between masking and classification tools, and instant, actionable insight into data risk, wherever your information lives. In other words, you spend less time firefighting, move faster, and can assure partners and customers that their data is in good hands.

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Request a demo and technical assessment to discover how Sentra’s AI-aware DSPM can speed up both your compliance and your innovation.

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Introduction: Why Data Access Governance Is Harder Than It Should Be

Data access governance should be simple: know where your sensitive data lives, understand who has access to it, and reduce risk without breaking business workflows. In practice, it’s rarely that straightforward. Modern organizations operate across cloud data stores, SaaS applications, AI pipelines, and hybrid environments. Data moves constantly, permissions accumulate over time, and visibility quickly degrades. Many teams turn to data access governance tools expecting clarity, only to find legacy platforms that are difficult to deploy, noisy, or poorly suited for dynamic, fast-proliferating cloud environments.

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A modern data access governance tool should provide continuous visibility into who and what can access sensitive data across cloud and SaaS environments, and help teams reduce overexposure safely and incrementally.

What Organizations Actually Need from Data Access Governance

Before evaluating vendors, it’s important to align on outcomes, just not features. Most teams are trying to solve the same core problems:

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  • Unified visibility across cloud data stores, SaaS platforms, and hybrid environments
  • Clear answers to “which identities have access to what, and why?”
  • Risk-based prioritization instead of long, unmanageable lists of permissions
  • Safe remediation that tightens access without disrupting workflows

Tools that focus only on periodic access reviews or static policies often fall short in dynamic environments where data and permissions change constantly.

Why Legacy and Over-Engineered Tools Fall Short

Many traditional data governance and IGA tools were designed for on-prem environments and slower change cycles. In cloud and SaaS environments, these tools often struggle with:

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  • Long deployment timelines and heavy professional services requirements
  • Excessive alert noise without clear guidance on what to fix first
  • Manual access certifications that don’t scale
  • Limited visibility into modern SaaS and cloud-native data stores

Overly complex platforms can leave teams spending more time managing the tool than reducing actual data risk.

Key Capabilities to Look for in a Modern Data Access Governance Tool

1. Continuous Data Discovery and Classification

A strong foundation starts with knowing where sensitive data lives. Modern tools should continuously discover and classify data across cloud, SaaS, and hybrid environments using automated techniques, not one-time scans.

2. Access Mapping and Exposure Analysis

Understanding data sensitivity alone isn’t enough. Tools should map access across users, roles, applications, and service accounts to show how sensitive data is actually exposed.

3. Risk-Based Prioritization

Not all exposure is equal. Effective platforms correlate data sensitivity with access scope and usage patterns to surface the highest-risk scenarios first, helping teams focus remediation where it matters most.

4. Low-Friction Deployment

Look for platforms that minimize operational overhead:

  • Agentless or lightweight deployment models
  • Fast time-to-value
  • Minimal disruption to existing workflows

5. Actionable Remediation Workflows

Visibility without action creates frustration. The right tool should support guided remediation, tightening access incrementally and safely rather than enforcing broad, disruptive changes.

How Teams Are Solving This Today

Security teams that succeed tend to adopt platforms that combine data discovery, access analysis, and real-time risk detection in a single workflow rather than stitching together multiple legacy tools. For example, platforms like Sentra focus on correlating data sensitivity with who or what can actually access it, making it easier to identify over-permissioned data, toxic access combinations, and risky data flows, without breaking existing workflows or requiring intrusive agents.

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The common thread isn’t the tool itself, but the ability to answer one question continuously:

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“Who can access our most sensitive data right now, and should they?”

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Teams using these approaches often see faster time-to-value and more actionable insights compared to legacy systems.

Common Gotchas to Watch Out For

When evaluating tools, buyers often overlook a few critical issues:

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  • Hidden costs for deployment, tuning, or ongoing services
  • Tools that surface risk but don’t help remediate it
  • Point-in-time scans that miss rapidly changing environments
  • Weak integration with identity systems, cloud platforms, and SaaS apps

Asking vendors how they handle these scenarios during a pilot can prevent surprises later.
Download The Dirt on DSPM POVs: What Vendors Don’t Want You to Know

How to Run a Successful Pilot

A focused pilot is the best way to evaluate real-world effectiveness:

  1. Start with one or two high-risk data stores
  2. Measure signal-to-noise, not alert volume
  3. Validate that remediation steps work with real teams and workflows
  4. Assess how quickly the tool delivers actionable insights

The goal is to prove reduced risk, not just improved reporting.

Final Takeaway: Visibility First, Enforcement Second

Effective data access governance starts with visibility. Organizations that succeed focus first on understanding where sensitive data lives and how it’s exposed, then apply controls gradually and intelligently. Combining DAG with DSPM is an effective way to achieve this.

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In 2026, the most effective data access governance tools are continuous, risk-driven, and cloud-native, helping security teams reduce exposure without slowing the business down.

Frequently Asked Questions (FAQs)

What is data access governance?

‍Data access governance is the practice of managing and monitoring who can access sensitive data, ensuring access aligns with business needs and security requirements.

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How is data access governance different from IAM?

‍IAM focuses on identities and permissions. Data access governance connects those permissions to actual data sensitivity and exposure, and alerts when violations occur.

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How do organizations reduce over-permissioned access safely?

‍By using risk-based prioritization and incremental remediation instead of broad access revocations.

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What should teams look for in a modern data access governance tool?

This question comes up frequently in real-world evaluations, including Reddit discussions where teams share what’s worked and what hasn’t. Teams should prioritize tools that give fast visibility into who can access sensitive data, provide context-aware insights, and allow incremental, safe remediation - all without breaking workflows or adding heavy operational overhead. Cloud- and SaaS-aware platforms tend to outperform legacy or overly complex solutions.

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