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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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Nikki Ralston

David Stuart

March 17, 2026

Min Read

Best Cloud Data Security Solutions for 2026

As enterprises scale cloud workloads and AI initiatives in 2026, cloud data security has become a board‑level priority. Regulatory frameworks are tightening, AI assistants are touching more systems, and sensitive data now spans IaaS, PaaS, SaaS, data lakes, and on‑prem.
‍

This guide compares four of the leading cloud data security solutions - Sentra, Wiz, Prisma Cloud, and Cyera - across:

‍

Architecture and deployment
Data movement and “toxic combination” detection
AI risk coverage and Copilot/LLM governance
Compliance automation and real‑world user sentiment

‍

Platform	Core Strength	Deployment Model	AI & Data Risk Coverage
Sentra	In-environment DSPM and AI-aware data governance, with strong focus on regulated data and unstructured stores	Purely agentless, in-place scanning in your cloud and data centers; optional lightweight on-prem scanners for file shares and databases	Shadow AI detection, M365 Copilot and AI agent inventory, data-flow mapping into AI pipelines, and guardrails for cloud and SaaS data
Wiz	Cloud-native CNAPP and Security Graph tying together data, identity, and cloud posture	Primarily agentless via cloud provider APIs and snapshots, with optional eBPF sensor for runtime context	Data lineage into AI pipelines via its security graph; AI exposure surfaced alongside misconfigurations and identity risk
Prisma Cloud	Code-to-cloud security, infrastructure risk, and compliance across multi-cloud	Hybrid: agentless scanning plus optional agents/sidecars for deep runtime protection	Tracks data movement into AI pipelines as part of attack-path analysis and compliance checks
Cyera	AI-native data discovery with converged DLP + DSPM for cloud data	Agentless, in-place scanning using local inspection or snapshots	AISPM and AI runtime protection for prompts, responses, and agents across SaaS and cloud environments

What Users Are Saying

Review platforms and field conversations surface patterns that go beyond feature matrices.

Sentra

Pros

Strong shadow data discovery, including legacy exports, backups, and unstructured sources like chat logs and call transcripts that other tools often miss
Built‑in compliance facilitation that reduces audit prep time for healthcare, financial services, and other regulated industries
In‑environment architecture that consistently appeals to privacy, risk, and data protection teams concerned about data residency and vendor data handling

Cons

Dashboards and reporting are powerful but can feel dense for first‑time users who aren’t familiar with DSPM concepts
Third‑party integrations are broad, but some connectors can lag when synchronizing very large environments

Wiz

Pros

Excellent multi‑cloud visibility and security graph that correlate misconfigurations, identities, and data assets for fast remediation
Well‑regarded customer success and responsive support teams

Cons

High alert volume if policies aren’t carefully tuned, which can overwhelm small teams
Configuration complexity grows with environment size and number of integrations

Prisma Cloud

Pros

Strong real‑time threat detection tightly coupled with major cloud providers, well suited to security operations teams
Proven scalability across large, hybrid environments combining containers, VMs, and serverless workloads

Cons

Cost is frequently cited as a concern in large‑scale deployments
Steeper learning curve that often requires dedicated training and ownership

Cyera

Pros

Smooth, agentless deployment with quick time‑to‑value for data discovery in cloud stores
Highly responsive support and strong focus on classification quality

Cons

Integration and operationalization complexity in larger enterprises, especially when folding into wider security workflows
Some backend customization and tuning require direct vendor involvement

Cloud Data Security Platforms: Architecture and Deployment

How a platform scans your data is as important as what it finds. Sending production data to a third‑party cloud for analysis can introduce its own risk, and regulators increasingly expect clear answers on where data is processed.

Sentra: In‑Environment DSPM for Regulated and AI‑Ready Data

Sentra takes a data‑first, in‑environment approach:

‍

Agentless connectors to cloud provider APIs and SaaS platforms mean sensitive content is scanned inside your accounts; it is never copied to Sentra’s cloud.
Lightweight on‑prem scanners extend coverage to file shares and databases, creating a unified view across IaaS, PaaS, SaaS, and on‑prem systems.

This design makes Sentra particularly attractive to organizations with strict data residency requirements and privacy‑driven governance models, especially in finance, healthcare, and other regulated sectors.

Wiz: Agentless CNAPP with Optional Runtime Sensors

Wiz is fundamentally agentless, connecting to cloud environments via APIs and leveraging temporary snapshots for inspection.

An optional eBPF‑based sensor adds runtime visibility for workloads without introducing inline latency.
The same security graph model underpins both infrastructure risk and emerging data/AI lineage features.

Prisma Cloud: Hybrid Agentless + Agent Model

Prisma Cloud combines:

Agentless scanning for vulnerabilities, misconfigurations, and compliance posture.
Optional agents or sidecars when deep runtime protection or granular workload telemetry is required.

This hybrid approach offers powerful coverage, but introduces more operational overhead than purely agentless DSPM platforms like Sentra and Cyera.

Cyera: In‑Place Cloud Data Inspection

Cyera focuses on in‑place data inspection, using local snapshots or direct connections to datastore APIs.

Sensitive data is analyzed within your environment rather than being shipped to a vendor cloud.
This aligns well with privacy‑first architectures that treat any external data processing as a risk to be minimized.

Identifying Toxic Combinations and Tracking Data Movement

Static discovery like, “here are your S3 buckets” is a basic capability. Real security value comes from correlating data sensitivity, effective access, and how data moves over time across clouds, regions, and environments.

Sentra: Data‑Aware Risk and End‑to‑End Data Flow Visibility

Sentra continuously maps your entire data estate, correlating classification results with IAM, ACLs, and sharing links to surface “toxic combinations” - high‑sensitivity data behind overly broad permissions.

‍

Tracks data movement across ETLs, database migrations, backups, and AI pipelines so you can see when production data drifts into dev, test, or unapproved regions.
Extends beyond primary databases to cover data lakes, analytics platforms, and modern big‑data formats in object storage, which are increasingly used as AI training inputs.

This gives security and data teams a living map of where sensitive data actually lives and how it moves, not just a static list of storage locations.

Wiz: Security Graph and CIEM

Wiz’s Security Graph maps identities, resources, configurations, and data stores in one model.

‍

Its CIEM capabilities aggregate effective permissions (including inherited policies and group memberships) to highlight over‑exposed data resources.
Wiz tracks data lineage into AI pipelines as part of its broader cloud risk view, helping teams understand where sensitive data intersects with ML workloads.

Prisma Cloud: Graph‑Based Attack Paths

Prisma Cloud uses a graph‑based risk engine to continuously simulate attack paths:

‍

Seemingly low‑risk misconfigurations and broad permissions are combined to identify chains that could expose regulated data.
The platform generates near real‑time alerts when data crosses geofencing boundaries or flows into unapproved analytics or AI environments.

Cyera: AI‑Native Classification and LLM Validation

Cyera pairs AI‑native classification with access analysis:

‍

It continuously scans structured and unstructured data for sensitive content, mapping who and what can reach each dataset.
An LLM‑based validation layer distinguishes real sensitive data from mock or synthetic data in dev/test, which can reduce false positives and cleanup noise.

AI Risk Detection: Shadow AI and Copilot Governance

Enterprise AI tools introduce a new class of risk: employees connecting business data to unauthorized models, or AI agents and copilots inheriting excessive access to legacy data.

Sentra: AI‑Ready Data Security and Copilot Guardrails

Sentra treats AI risk as a data problem:

‍

Tracks data flows between sources and destinations and compares them against an inventory of approved AI tools, flagging when sensitive data is routed to unauthorized LLMs or agents.
For Microsoft 365 Copilot, Sentra builds a catalog of data across SharePoint, OneDrive, and Teams, mapping which users and groups can access each set of documents and providing guardrails before Copilot is widely rolled out.

This gives security teams a practical definition of AI data readiness: knowing exactly which data AI can see, and shrinking that blast radius before something goes wrong.

Cyera: AISPM and AI Runtime Protection

Cyera takes a dual‑layer approach to AI risk:

AI Security Posture Management (AISPM) inventories sanctioned and unsanctioned AI tools and maps which sensitive datasets each can access.
AI Runtime Protection monitors prompts, responses, and agent actions in real time, blocking suspicious activity such as data leakage or prompt‑injection attempts.

For M365 Copilot Studio, Cyera integrates with Microsoft Entra’s agent registry to track AI agents and their data scopes.

Wiz and Prisma Cloud: AI as Part of Data Lineage

Wiz and Prisma Cloud both treat AI as an extension of their data lineage and attack‑path capabilities:

They track when sensitive data enters AI pipelines or training environments and how that intersects with misconfigurations and identity risk.
However, they do not yet offer the same depth of AI‑specific governance controls and runtime protections as dedicated AI‑aware platforms like Sentra and Cyera.

Compliance Automation and Framework Mapping

For teams preparing for GDPR, HIPAA, PCI, SOC 2, or EU AI Act reviews, manually mapping findings to control sets and assembling evidence is slow and error‑prone.

Platform Approaches to Compliance

Platform	Compliance Approach
Wiz	Maps cloud and workload findings to 100+ built-in frameworks (including GDPR, HIPAA, and the EU AI Act).
Prisma Cloud	Automates mapping to major frameworks’ control requirements with audit-ready documentation, often completing large assessments in minutes to under an hour.
Sentra	Focuses on regulated data visibility and privacy-driven governance; its in-environment DSPM, classification accuracy, and reporting are frequently cited by users as key to simplifying data-centric audit prep and proving control over sensitive data. Provides petabyte-scale assessments within hours and consolidated evidence for auditors.
Cyera	Provides real-time visibility and automated policy enforcement; supports compliance reporting, though public documentation is less explicit on automatic mapping to specific, named control sets.

‍

Sentra is especially compelling when audits hinge on where regulated data actually lives and how it is governed, rather than just infrastructure posture.

Choosing Among the Best Cloud Data Security Solutions

All four platforms address real, pressing needs—but they are not interchangeable.

Choose Sentra if you need strict in‑environment data governance, high‑precision discovery across cloud, SaaS, and on‑prem, and AI‑aware guardrails that make Copilot and other AI deployments provably safer—without moving sensitive data out of your own infrastructure.
Choose Wiz if your top priority is broad cloud security coverage and a unified graph for vulnerabilities, misconfigurations, identities, and data across multi‑cloud at scale.
Choose Prisma Cloud if you want a code‑to‑cloud platform that ties data exposure to DevSecOps pipelines and workload runtime protection, and you have the resources to operationalize its breadth.
Choose Cyera if you’re focused on AI‑native classification and a converged DLP + DSPM motion for large volumes of cloud data, and you’re prepared for a more involved integration phase.

For most mature security programs, the question isn’t whether to adopt these tools but how to layer them:

‍

A CNAPP for cloud infrastructure risk
A DSPM platform like Sentra for data‑first visibility and AI readiness
DLP/SSE for enforcement at egress and user edges
Compliance automation to translate all of that into evidence your auditors, regulators, and board can trust

Taken together, this stack lets you move faster in the cloud and with AI, without losing control of the data that actually matters.

‍

Nikki Ralston

David Stuart

March 16, 2026

Min Read

How to Protect Sensitive Data in AWS

Storing and processing sensitive data in the cloud introduces real risks, misconfigured buckets, over-permissive IAM roles, unencrypted databases, and logs that inadvertently capture PII. As cloud environments grow more complex in 2026, knowing how to protect sensitive data in AWS is a foundational requirement for any organization operating at scale. This guide breaks down the key AWS services, encryption strategies, and operational controls you need to build a layered defense around your most critical data assets.

How to Protect Sensitive Data in AWS (With Practical Examples)

Effective protection requires a layered, lifecycle-aware strategy. Here are the core controls to implement:

Field-Level and End-to-End Encryption

Rather than encrypting all data uniformly, use field-level encryption to target only sensitive fields, Social Security numbers, credit card details, while leaving non-sensitive data in plaintext. A practical approach: deploy Amazon CloudFront with a Lambda@Edge function that intercepts origin requests and encrypts designated JSON fields using RSA. AWS KMS manages the underlying keys, ensuring private keys stay secure and decryption is restricted to authorized services.

Encryption at Rest and in Transit

Enable default encryption on all storage assets, S3 buckets, EBS volumes, RDS databases. Use customer-managed keys (CMKs) in AWS KMS for granular control over key rotation and access policies. Enforce TLS across all service endpoints. Place databases in private subnets and restrict access through security groups, network ACLs, and VPC endpoints.

Strict IAM and Access Controls

Apply least privilege across all IAM roles. Use AWS IAM Access Analyzer to audit permissions and identify overly broad access. Where appropriate, integrate the AWS Encryption SDK with KMS for client-side encryption before data reaches any storage service.

Automated Compliance Enforcement

Use CloudFormation or Systems Manager to enforce encryption and access policies consistently. Centralize logging through CloudTrail and route findings to AWS Security Hub. This reduces the risk of shadow data and configuration drift that often leads to exposure.

What Is AWS Macie and How Does It Help Protect Sensitive Data?

AWS Macie is a managed security service that uses machine learning and pattern matching to discover, classify, and monitor sensitive data in Amazon S3. It continuously evaluates objects across your S3 inventory, detecting PII, financial data, PHI, and other regulated content without manual configuration per bucket.

‍

Key capabilities:

‍

Generates findings with sensitivity scores and contextual labels for risk-based prioritization
Integrates with AWS Security Hub and Amazon EventBridge for automated response workflows
Can trigger Lambda functions to restrict public access the moment sensitive data is detected
Provides continuous, auditable evidence of data discovery for GDPR, HIPAA, and PCI-DSS compliance

Understanding what sensitive data exposure looks like is the first step toward preventing it. Classifying data by sensitivity level lets you apply proportionate controls and limit blast radius if a breach occurs.

AWS Macie Pricing Breakdown

Macie offers a 30-day free trial covering up to 150 GB of automated discovery and bucket inventory. After that:

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Component	Cost
S3 bucket monitoring	$0.10 per bucket/month (prorated daily), up to 10,000 buckets
Automated discovery	$0.01 per 100,000 S3 objects/month + $1 per GB inspected beyond the first 1 GB
Targeted discovery jobs	$1 per GB inspected; standard S3 GET/LIST request costs apply separately

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For large environments, scope automated discovery to your highest-risk buckets first and use targeted jobs for periodic deep scans of lower-priority storage. This balances coverage with cost efficiency.

What Is AWS GuardDuty and How Does It Enhance Data Protection?

AWS GuardDuty is a managed threat detection service that continuously monitors CloudTrail events, VPC flow logs, and DNS logs. It uses machine learning, anomaly detection, and integrated threat intelligence to surface indicators of compromise.

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What GuardDuty detects:

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Unusual API calls and atypical S3 access patterns
Abnormal data exfiltration attempts
Compromised credentials
Multi-stage attack sequences correlated from isolated events

Findings and underlying log data are encrypted at rest using KMS and in transit via HTTPS. GuardDuty findings route to Security Hub or EventBridge for automated remediation, making it a key component of real-time data protection.

Using CloudWatch Data Protection Policies to Safeguard Sensitive Information

Applications frequently log more than intended, request payloads, error messages, and debug output can all contain sensitive data. CloudWatch Logs data protection policies automatically detect and mask sensitive information as log events are ingested, before storage.

How to Configure a Policy

Create a JSON-formatted data protection policy for a specific log group or at the account level
Specify data types to protect using over 100 managed data identifiers (SSNs, credit cards, emails, PHI)
The policy applies pattern matching and ML in real time to audit or mask detected data

Important Operational Considerations

Only users with the logs:Unmask IAM permission can view unmasked data
Encrypt log groups containing sensitive data using AWS KMS for an additional layer
Masking only applies to data ingested after a policy is active, existing log data remains unmasked
Set up alarms on the LogEventsWithFindings metric and route findings to S3 or Kinesis Data Firehose for audit trails

Implement data protection policies at the point of log group creation rather than retroactively, this is the single most common mistake teams make with CloudWatch masking.

How Sentra Extends AWS Data Protection with Full Visibility

Native AWS tools like Macie, GuardDuty, and CloudWatch provide strong point-in-time controls, but they don't give you a unified view of how sensitive data moves across accounts, services, and regions. This is where minimizing your data attack surface requires a purpose-built platform.

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What Sentra adds:

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Discovers and governs sensitive data at petabyte scale inside your own environment, data never leaves your control
Maps how sensitive data moves across AWS services and identifies shadow and redundant/obsolete/trivial (ROT) data
Enforces data-driven guardrails to prevent unauthorized AI access
Typically reduces cloud storage costs by ~20% by eliminating data sprawl

Knowing how to protect sensitive data in AWS means combining the right services, KMS for key management, Macie for S3 discovery, GuardDuty for threat detection, CloudWatch policies for log masking, with consistent access controls, encryption at every layer, and continuous monitoring. No single tool is sufficient. The organizations that get this right treat data protection as an ongoing operational discipline: audit IAM policies regularly, enforce encryption by default, classify data before it proliferates, and ensure your logging pipeline never exposes what it was meant to record.

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Daniel Suissa

March 15, 2026

Min Read

The Blind Spot in Your Data Lake: Why Big Data Format Scanning Is the Next Frontier of Data Security

Data lakes were supposed to be the great democratizer of enterprise analytics. Centralized, scalable, and cost-effective, they promised to put data in the hands of every team that needed it. And they delivered -- perhaps too well. Today, petabytes of sensitive data sit in Apache Parquet files, Avro containers, and ORC stores across S3 buckets, Azure Data Lake Storage, and Google Cloud Storage, often with little to no visibility into what those files actually contain.

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Traditional Data Loss Prevention (DLP) tools were built for a world of emails, PDFs, and spreadsheets. They have no understanding of columnar storage formats, embedded schemas, or the sheer scale of modern data lake architectures. That gap is where sensitive data hides in plain sight -- and where Sentra's data lake format scanning changes the equation entirely.

The Shadow Data Problem in Data Lakes

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Every modern enterprise runs some version of the same playbook: production databases feed into ETL pipelines, which land data in object storage as Parquet, Avro, or ORC files. Data engineers, analysts, and machine learning teams then consume that data downstream.

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The security problem is straightforward but pervasive. When data engineering teams copy production data into data lakes for analytics, the PII that was supposed to be masked or anonymized often arrives intact. A full copy of customer records -- Social Security numbers, credit card numbers, health information -- ends up in a Parquet file in a shared S3 bucket, accessible to anyone with the right IAM role.

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This is not a hypothetical scenario. It is the default state of most enterprise data lakes. And with data democratization initiatives actively expanding access to these stores, the blast radius of unprotected data lake files grows with every new user who gets read permissions.

Why Traditional DLP Falls Short

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Conventional DLP solutions treat files as opaque blobs of text. They can scan a CSV or a Word document, but hand them an Apache Parquet file and they see nothing. This is a fundamental architectural limitation, not a feature gap that can be patched.

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Big data formats are structurally different from traditional file types. Parquet and ORC use columnar storage, meaning data is organized by column rather than by row. Avro embeds its schema directly in the file. Arrow IPC (Feather) uses an in-memory format optimized for zero-copy reads. Scanning these formats requires purpose-built readers that understand their internal structure -- readers that traditional DLP simply does not have.

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The result is a compliance blind spot that grows larger every quarter as more data moves into lakehouse architectures powered by Databricks, Snowflake external tables, and similar platforms.

How Sentra Scans Big Data Formats

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Sentra provides native, schema-aware scanning for the full spectrum of data lake file formats. This is not a bolt-on capability -- it is core to how our platform understands modern data infrastructure.

Apache Parquet

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Parquet is the lingua franca of the modern data lake. Sentra's tabular reader processes Parquet files with full awareness of their columnar structure, performing intelligent column-level classification. Rather than brute-forcing through every byte, Sentra leverages the columnar layout to efficiently scan individual columns for sensitive data patterns. Batch processing support means even large Parquet datasets are handled without requiring the entire file to be loaded into memory at once. Sentra also recognizes Spark checkpoint files (the `c000` convention) and processes them via Parquet or JSON fallback, ensuring that intermediate pipeline outputs do not escape scrutiny. Sentra also goes beyond the parquet schema and detects nested schemas like a json column that hides behind a “string” data type, adding meaningful context to the classification engine.

Apache Avro

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Avro files carry their schema with them, and Sentra takes full advantage of that. Our tabular reader parses the embedded schema to understand field names, types, and structure before scanning the data itself. This schema-aware approach enables more accurate classification -- a field named `ssn` containing nine-digit numbers is treated differently than a field named `zip_code` with the same pattern.

Apache ORC

The Optimized Row Columnar format is a staple of Hive-based data warehouses and remains widely used across Hadoop-era data infrastructure. Sentra's tabular reader handles ORC files natively, applying the same column-level classification intelligence used for Parquet and Avro.

Apache Feather / Arrow IPC

Arrow's IPC format (commonly known as Feather) is increasingly used for fast data interchange between Python, R, and other analytics tools. Sentra scans these files through its textual reader, ensuring that even ephemeral interchange formats do not become a vector for untracked sensitive data.

Column-Level Intelligence

Across all of these formats, Sentra performs column-level scanning and classification. This is critical at data lake scale. A single column in a petabyte Parquet dataset could contain millions of Social Security numbers, while every other column holds benign operational metrics. Column-level granularity means Sentra can pinpoint exactly where sensitive data lives, rather than simply flagging an entire file as "contains PII."

The Compliance Imperative

Regulatory frameworks do not carve out exceptions for big data formats. GDPR's right of access and right to erasure apply regardless of whether personal data is stored in a PostgreSQL table or a Parquet file in S3. CCPA's disclosure requirements extend to every copy of consumer data, including the one sitting in your analytics data lake.

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Data Subject Access Requests (DSARs) are particularly challenging when sensitive data is spread across thousands of Parquet files in a data lake. Without automated scanning that understands these formats, responding to a DSAR becomes a manual archaeology project -- expensive, slow, and error-prone.

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The AI governance dimension adds another layer of urgency. Machine learning training datasets are frequently stored in Parquet format. If those datasets contain PII that was used to train models, organizations face regulatory exposure under emerging AI governance frameworks. Knowing what personal data exists in your ML training pipelines is no longer optional -- it is a compliance requirement that is rapidly taking shape across jurisdictions.

From Blind Spot to Full Visibility

The shift to data lakehouse architectures is accelerating. Databricks, Snowflake, and the broader modern data stack have made it easier than ever to store and process massive volumes of data in open file formats. That is a net positive for analytics and engineering teams. But without security tooling that speaks the same language as the data infrastructure, sensitive data will continue to accumulate in places where no one is looking.

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Sentra closes that gap. By providing native, schema-aware scanning for Parquet, Avro, ORC, Feather, and related formats -- combined with intelligent column-level classification and efficient batch processing -- Sentra gives security and compliance teams the visibility they need into the fastest-growing data stores in the enterprise.

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Data lakes are not going away. The question is whether your security posture can keep up with the data engineering teams that feed them. With Sentra, the answer is yes.

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*Sentra is a Data Security Posture Management (DSPM) platform that automatically discovers, classifies, and monitors sensitive data across your entire cloud environment. To learn more about how Sentra handles data lake scanning and 150+ other file formats, book a demo with our data security experts.

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