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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.

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‍

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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One of the most interesting blind spots I see in mature security programs isn’t a database or a SaaS app. It’s web archives.

If you’re in financial services, you may be required to archive every version of your public website for years. Legal teams preserve web content under hold. Marketing and product teams crawl competitors for competitive intel. Security teams capture phishing pages and breach sites for analysis. All of that activity produces WARC and ARC files - standard formats for storing captured web content.

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Now ask yourself: what’s in those archives?

Where Web Archives Come From and Why They Get Ignored

In most enterprises, web archives are created in predictable ways, but rarely treated as data stores that need to be actively managed. Compliance teams crawl and preserve marketing pages, disclosures, and rate sheets to meet record-keeping requirements. Legal teams snapshot websites for e-discovery and retain those captures for years. Product and growth teams scrape competitor sites, pricing pages, and documentation, while security teams collect phishing kits, fake login pages, and breach sites for analysis.

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All of this content ends up stored as WARC or ARC files in object storage or file shares. Once the initial crawl is complete and the compliance requirement is satisfied, these archives are typically dumped into an S3 bucket or on-prem share, referenced in a ticket or spreadsheet, and then quietly forgotten.

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That’s where the risk begins. What started as a compliance or research activity turns into a growing, unmonitored data store - one that may contain sensitive and regulated information, but sits outside the scope of most security and privacy programs.

What’s Really Inside a WARC or ARC File?

A single WARC from a routine compliance crawl of your own site can contain thousands of pages. Many of those pages will have:

‍

Customer names and emails
Account IDs and usernames
Phone numbers and mailing addresses
Perhaps even partial transaction details in page content, forms, or query strings

If you’re scraping external sites, those files can hold third‑party PII: profiles, contact details, and public record data. Threat intel archives may include:

‍

Captured credentials from phishing kits
Breach data and exposed account information
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Meanwhile, the archives themselves grow quietly in S3 buckets and on‑prem file shares, rarely revisited and almost never scanned with the same rigor you apply to “primary” systems.

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From a privacy perspective, this is a real problem. Under GDPR and similar laws, individuals have the right to request access to and deletion of their personal data. If that data lives inside a 3‑year‑old WARC file you can’t even parse, you have no practical way or scalable way to honor that request. Multiply that across years of compliance archiving, legal holds, scraping campaigns, and threat intel crawls, and you’re sitting on terabytes of unmanaged web content containing PII and regulated data.

Why Traditional DLP and Discovery Can’t Handle WARC and ARC

Most traditional DLP (Data Loss Prevention) and data discovery tools were designed for a simpler data landscape, focused on emails, attachments, PDFs, Office documents, and flat text logs or CSV files. When these tools encounter formats like WARC or ARC files, they typically treat them as opaque blobs of data, relying on basic text extraction and regex-based pattern matching to identify sensitive information.

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This approach breaks down with web archives. WARC and ARC files are complex container formats that store full HTTP interactions, including requests, responses, headers, and payloads. A single web archive can contain thousands of captured pages and resources: HTML, JavaScript, CSS, JSON APIs, images, and PDFs, often compressed or encoded in ways that require reconstructing the original HTTP responses to interpret correctly.

‍

As a result, legacy DLP tools cannot reliably parse or analyze WARC and ARC files. Instead, they surface only fragmented data such as headers, binary content, or partial HTML, without reconstructing the full user-visible context. This means they miss critical elements like complete web pages, DOM structures, form inputs, query strings, request bodies, and embedded assets where sensitive data such as PII, credentials, or financial information may exist.

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The result is a significant compliance and security gap. Web archives stored in WARC and ARC formats often contain regulated data but remain unscanned and unmanaged, creating a persistent blind spot for traditional DLP and DSPM programs.

How Sentra Scans Web Archives at Scale

We built web archive scanning into Sentra to make this tractable.

Sentra’s WarcReader understands both WARC and ARC formats. It:

‍

Processes captured HTTP responses, not just headers
Extracts the actual HTML page content and associated resources from each record
Normalizes those payloads so they can be scanned just like any other web‑delivered content

Once we’ve pulled out the page content and resources, we run them through the same classification engine we apply to your other data stores, looking for:

‍

PII (names, emails, addresses, national IDs, phone numbers, etc.)
Financial data (account numbers, card numbers, bank details)
Healthcare information and PHI indicators
Credentials and other secrets
Business‑sensitive data (internal IDs, case numbers, etc.)

Because WARC files can be huge, we do all of this in memory, without unpacking archives to disk. That matters for two reasons:

‍

Performance and scale: We can stream through large archives without creating temporary, unmanaged copies.
Security: We avoid writing decrypted or reconstructed content to local disks, which would create new artifacts you now have to protect.

We also handle embedded resources - images, documents, and other files captured as part of the original pages — so you’re not only seeing what was in the HTML but also what was linked or rendered alongside it. Sentra’s existing file parsers and OCR engine can inspect those nested assets for sensitive content just as they would in any other data store.

Bringing Web Archives into Your DSPM Program

Once you can actually see inside web archives, you can bring them into your data security program instead of pretending they’re “just logs.”

With Sentra, teams can:

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Discover where web archives live across cloud and on‑prem (S3, Azure Blob, GCS, NFS/SMB shares, and more).
Classify the captured content for PII, PCI, PHI, credentials, and business‑sensitive information.
Assess regulatory exposure from long‑running archiving programs and legal holds that have accumulated unmanaged PII over time.
Support DSAR and deletion workflows that touch archived content, so you can respond to GDPR/CCPA requests with an honest inventory that includes historical web captures.
Evaluate scraping and threat‑intel collections to identify sensitive data they were never supposed to capture in the first place (for example, credentials, breach records, or third‑party PII).

In practice, this often leads to concrete actions like:

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Tightening retention policies on specific archive sets
Segmenting or encrypting archives that contain regulated data
Updating crawler configurations to avoid collecting sensitive content going forward
Aligning privacy teams, legal, and security around a shared understanding of what’s actually in years’ worth of WARC/ARC content

Web Archives Are Data Stores - Treat Them That Way

Web archives aren’t just compliance artifacts, they’re data stores, often holding sensitive and regulated information. Yet in most organizations, WARC and ARC files sit outside the scope of DSPM and data discovery, creating a blind spot between what’s stored and what’s actually secured.

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Sentra removes that tradeoff. You can keep the archives you’re required to maintain and gain full visibility into the data inside them. By bringing WARC and ARC files into your DSPM program, you extend coverage to web archives and other hard-to-reach data—without changing how you store or manage them.

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Want to see what’s hiding in your web archives? Explore how Sentra scans WARC and ARC files and uncovers sensitive data at scale.

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Ask any security engineer how they feel about DLP alerts and you’ll usually get the same reaction. They are drowning in them. Over the last decade, DLP has built a reputation for noisy alerts, rigid rules, and confusing dashboards that bury real risk under a mountain of “maybe” events.

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Teams roll out endpoint, email, and network DLP, wire in SaaS connectors, and import standard PCI/PII templates. Within weeks, analysts are triaging hundreds of alerts a day, most of which turn out to be benign. Business users complain that normal work is blocked, so policies get carved up with exceptions or quietly disabled. Meanwhile, the most sensitive data quietly spreads into collaboration tools, cloud storage, and AI workflows that DLP never sees.

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The problem is that DLP is being asked to do too much on its own: discover sensitive data, understand its business context, and enforce policies in motion, all from a narrow view of each channel. To fix false positives in a durable way, you have to stop treating DLP as the brain of your data security program and give it an actual data-intelligence layer to work with.

That’s the role of modern Data Security Posture Management (DSPM).

Why Traditional DLP Can Be So Noisy

Most DLP engines still lean heavily on pattern matching and static rules. They look for strings that resemble card numbers, social security numbers, or keywords, and they try to infer “sensitive vs. not” from whatever they can see in a single email, file, or HTTP transaction. That approach might have been tolerable when most sensitive data sat in a few on‑prem systems, but it doesn’t scale to multi‑cloud, SaaS, and AI‑driven environments.

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In practice, three things tend to go wrong:

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First, DLP rarely has full visibility. Sensitive data now lives in cloud data lakes, SaaS apps, shared drives, ticketing systems, and AI training sets. Many of those locations are either out of reach for traditional DLP or only partially covered.

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Second, the rules themselves are crude. A nine‑digit number might be a government ID, or it might be an internal ticket number. A CSV export might be an innocuous test file or a real production dump. Without a shared understanding of what the data actually represents, rules fire on look‑alikes and miss real exposures.

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Third, each DLP product, the endpoint agent, the email gateway, the CASB, tries to solve classification locally. You end up with inconsistent detections and competing definitions of “sensitive” that don’t match what the business actually cares about. When you add those up, it’s no surprise that false positives consume so much analyst time and so much political capital with the business.

How DSPM Changes the Equation

DSPM was designed to separate what DLP has been trying to do into dedicated layers. Instead of asking DLP to discover, classify, and enforce all at once, DSPM owns discovery and classification, and DLP focuses on enforcement.

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A DSPM platform like Sentra connects directly, via APIs and in‑environment scanning, to your cloud, SaaS, and on‑prem data stores. It builds a unified inventory of data, then uses AI‑driven models and domain‑specific logic to decide:

What is this object?
How sensitive is it?
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Who or what can currently access it?

From there, DSPM applies consistent labels to that data, often using frameworks like Microsoft Purview Information Protection (MPIP) so labels are understood by other tools. Those labels are then pushed into your DLP stack, SSE/CASB, and email and endpoint controls, so every enforcement point is working from the same definition of sensitivity, instead of guessing on the fly.

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Once DLP is enforcing on clear labels and context, rather than raw patterns, you no longer need dozens of almost‑duplicate rules per channel. Policies become simpler and more precise, which is what allows teams to realistically drive false positives down by up to half or more.

A Practical Approach to Cutting DLP Noise

If your security team is exhausted by DLP alerts today, you don’t need another round of regex tuning. You need a change in operating model. A pragmatic sequence looks like this.

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Start by measuring the problem instead of just reacting to it. Capture how many DLP alerts you see per week, how many of those are ultimately dismissed, and how much analyst time they consume. Pay special attention to the policies and channels that generate the most noise, because that’s where you’ll see the biggest benefit from a DSPM‑driven approach.

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Next, work with DSPM to turn your noisiest rules into label‑driven policies. Instead of “block any message that looks like it contains a card number,” express the rule as “block files labeled PCI sent to personal domains” or “quarantine emails carrying PHI labels to unapproved partners.” Once Sentra or another DSPM platform is reliably applying those labels, DLP simply has to enforce on them.

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Then, add business context. The same file can be benign in one context and dangerous in another. Combine labels with identity, role, channel, and basic behavior signals like, time of day, destination, volume, etc., so that only genuinely suspicious events result in hard blocks or escalations. A finance export labeled ‘Confidential’ going to an approved auditor should not be treated the same as that export leaving for an unknown Gmail account at midnight.

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Finally, create a feedback loop. Allow analysts to flag alerts as false positives or misconfigurations, and give users controlled ways to override with justification in edge cases. Feed that information back into DSPM tuning and DLP policies at a regular cadence, so your classification and rules get closer to how the business actually operates.

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Over time, you’ll find that you write fewer DLP rules, not more. The rules you do have are easier to explain to stakeholders. And most importantly, your analysts spend their time on true positives and meaningful insider‑risk investigations, not on the hundredth low‑value alert of the week.

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At that point, you haven’t just made DLP tolerable. You’ve turned it into a quiet, reliable enforcement layer sitting on top of a data‑intelligence foundation.

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Sensitive data discovery has become the front door to everything that matters in data security: AI readiness, Microsoft 365 Copilot governance, continuous compliance, and whether your DLP actually works. The days of simply scanning a few databases before an audit are over. Your riskiest information now lives in cloud warehouses, SaaS apps, PDFs, call recordings, and AI pipelines; and most security teams are trying to keep up with tools that were built for a different era.

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If you’re evaluating the best sensitive data discovery tools today, you’ll almost certainly encounter Sentra, BigID, Varonis, and Cyera. All four have credibility in the market. Though they are not interchangeable, especially if you care about AI data security, multi‑cloud DSPM, and keeping data inside your own environment.

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Below is a comparison that reflects what each platform delivers in 2026, followed by a deeper look at where each one fits and why Sentra is increasingly the default choice for AI‑scale, cloud‑first enterprises.

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Side‑by‑Side: Sentra vs BigID vs Varonis vs Cyera

The chart below focuses on the dimensions security and data leaders ask about most often: architecture, coverage, classification quality, AI support, real‑time controls, scale, and fit.

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Capability	Sentra	BigID	Varonis	Cyera
Architecture & where data lives	Cloud-native, agentless platform that scans data in-place across clouds, SaaS, and on-prem. Data never leaves the customer environment; only metadata and findings are processed.	Cloud-centric discovery platform with SaaS control plane. Often relies on connectors and moving metadata or samples into its environment for analysis.	Built around on-prem collectors and agents. Deploys locally but sends metadata to its platform for analytics.	Cloud-native DSPM with agentless approach, but often requires data or metadata to leave the environment for analysis.
Coverage	Broadest coverage across IaaS, PaaS, SaaS, and on-prem, including structured and unstructured data.	Very broad connectors across SaaS and data platforms, but depends on configuration.	Strong for unstructured and on-prem; cloud and SaaS coverage improving.	Good cloud/SaaS coverage but weaker on-prem and structured depth.
Classification quality	AI/ML-enhanced with >98% accuracy and deep business context (ownership, sensitivity, purpose).	Strong classification but higher false negatives in complex scenarios.	Rich classifiers but complex tuning and heavier rescans.	Less contextual, higher false positives, more validation required.
AI & Copilot security	Purpose-built for AI risks: Copilot readiness, agent inventory, data access mapping, identity-based guardrails.	Strong governance via Purview but less unified AI security view.	Emerging AI use cases, not core focus.	LLM-based validation but limited visibility into AI data movement.
DSPM + DAG + DDR	Unified platform combining posture, access governance, and detection/response in real time.	Strong discovery and privacy workflows; relies on integrations for detection.	Very strong DAG for permissions, limited DDR for cloud threats.	DSPM-focused; no native DDR and limited real-time threat linkage.
Time to value	Fast agentless deployment; insights day one, full coverage in days.	Heavier setup with connectors and integrations.	Long deployment cycles due to agents and integrations.	Quick start but slower full inventory at scale.
Scale & cost	Petabyte-scale efficiency; scans tens of PB in days with very low cost.	Predictable pricing but higher compute cost at scale.	Higher operational cost at large scale.	Scales but with higher resource consumption and cost.
Best fit	Large cloud-first enterprises needing unified DSPM, DAG, DDR and AI governance.	Organizations prioritizing privacy workflows and Microsoft ecosystem.	Enterprises focused on on-prem file security and permissions.	Cloud-native DSPM use cases with narrower scope.

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How to Read This Chart (Without the Hype)

All four of these tools can legitimately call themselves sensitive data discovery platforms:

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Sentra is built as a cloud‑native DSPM + DAG + DDR platform that keeps data in your environment, with strong AI data readiness and copilot coverage.
BigID is often chosen for privacy, DSAR, and broad connector needs, especially in Microsoft‑heavy environments.
Varonis remains a heavyweight for on‑prem file servers and unstructured data with deep permission analytics.
Cyera focuses on cloud‑native DSPM with agentless posture scanning and some AI‑driven validation.

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Where they diverge is in how far they go beyond “finding data”:

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Some stop at discovery and classification, leaving access, AI governance, and response to other tools.
Others focus on specific environments (for example, on‑prem files or S3‑only) and leave gaps in SaaS, AI pipelines, or PDFs, audio, and video.
Only a Sentra offers in‑place, multi‑cloud coverage with continuous DSPM, DAG, and DDR at truly large scale.

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That’s the lens where Sentra consistently looks strongest, especially if you’re already piloting or rolling out M365 Copilot and other GenAI assistants or have petabytes of regulated data across multi-cloud and hybrid infrastructure.

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Why Sentra Is the Best Fit for AI‑Scale, Multi‑Cloud Discovery

Senra emerges as a clear leader because tt is designed for organizations that:

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Run at petabyte scale across AWS, Azure, GCP, SaaS, and on‑prem.
Are under regulatory pressure to show continuous control over PII, PHI, PCI, and IP.
Are rolling out GenAI and AI copilots but can’t afford accidental data exposure.

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A few traits make Sentra stand out:

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Everything is in‑place and agentless.
Discovery and classification run inside your cloud accounts and data centers using APIs and serverless scanners. Sensitive data isn’t copied into a vendor environment for processing, and scanning doesn’t depend on a forest of agents. That’s both a security benefit and a deployment advantage.

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Sentra understands the data and the business around it.
Sentra’s AI classifier doesn’t stop at matching patterns. It delivers >98% accuracy across structured and unstructured data, and it attaches rich business context: which department owns the data, where it resides geographically, whether it’s synthetic or real, and what role it plays in the business. That context directly drives risk scoring, prioritization, and automated remediation.

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Sentra treats audio, video, and PDFs as first‑class data sources.
Sentra scans dozens of audio and video formats by extracting and transcribing audio with ML models, then running the same classifiers used for text. It also parses complex PDFs, runs OCR on scanned pages, and inspects metadata - all inside your cloud. That closes some of the biggest blind spots in legacy DLP and discovery tools.

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Sentra scales to petabytes without breaking the bank.
Internal and customer bake‑offs show Sentra scanning 9 PB in under 72 hours, with the architecture designed to cover hundreds of petabytes in days and deliver around 10x lower scan cost than older approaches. That makes continuous discovery and re‑scanning feasible instead of a once‑a‑year luxury.

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Sentra unifies DSPM, DAG, and DDR.
Instead of scattering posture, access, and detection across separate siloed tools, Sentra ties them together. It shows you where sensitive data is, who or what can access it, how it’s being used, and what needs to happen next - from revoking access to applying labels or opening tickets - in one place.

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So Which “Best Sensitive Data Discovery Tool” Should You Choose?

If you are primarily focused on:

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Privacy and DSAR workflows with deep governance in a Microsoft‑centric stack, BigID will be on your shortlist.
On‑prem file security and permissions analytics for legacy environments, Varonis still deserves serious consideration.
Cloud‑only DSPM posture checks with agentless deployment and LLM‑augmented validation, Cyera may be attractive in narrower, less regulated scenarios.

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But if you need a single, AI‑ready data security platform that:

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Discovers and classifies sensitive data across multi‑cloud, SaaS, and on‑prem,
Keeps data inside your environment while doing it,
Powers DSPM, DAG, DDR, M365 Copilot governance, and DLP from one consistent data‑context layer, and
Scales to petabytes without turning each scan into a budgeting exercise,

Then Sentra is, in practice, the best‑fit choice among today’s leading sensitive data discovery tools.

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Use Redshift Data Scrambling for Additional Data Protection

The Importance of Data Protection

What is Redshift Data Masking?

What is Redshift Data Scrambling?

Data Masking vs. Data Scrambling

Setting up Redshift Data Scrambling

Step 1: Create a new Redshift cluster

Step 2: Define a scrambling key

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

Step 4: Apply the UDF to sensitive columns

Step 5: Test and validate the scrambled data

Step 6: Monitor and maintain the scrambled data

Different Options for Scrambling Data in Redshift

Random

Shuffle

Reversible

Custom

Best Practices for Using Redshift Data Scrambling

Use Unique Keys for Each Table

Encrypt Sensitive Data Fields

Use Strong Encryption Algorithms

Control Access to Encryption Keys

Regularly Rotate Encryption Keys

Turn on logging

Monitor Performance

Establishing Backup and Disaster Recovery

Frequent Review and Updates

Decrypting Data in Redshift

Step 1: Create a UDF with decryption logic for substitution

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

Conclusion

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Best Sensitive Data Discovery Tools in 2026

Best Sensitive Data Discovery Tools in 2026

Side‑by‑Side: Sentra vs BigID vs Varonis vs Cyera

How to Read This Chart (Without the Hype)

Why Sentra Is the Best Fit for AI‑Scale, Multi‑Cloud Discovery

So Which “Best Sensitive Data Discovery Tool” Should You Choose?

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