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Use Redshift Data Scrambling for Additional Data Protection

May 3, 2023

Min Read

Team Sentra

Team Sentra | Cybersecurity Experts

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.

‍

Team Sentra

Team Sentra | Cybersecurity Experts

Read insightful articles by the Sentra team about different topics, such as, preventing data breaches, securing sensitive data, and more.

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Ron Reiter

November 17, 2024

Min Read

AI and ML

Enhancing AI Governance: The Crucial Role of Data Security

In today’s hyper-connected world, where big data powers decision-making, artificial intelligence (AI) is transforming industries and user experiences around the globe. Yet, while AI technology brings exciting possibilities, it also raises pressing concerns, particularly related to security, compliance, and ethical integrity.

As AI adoption accelerates一fueled by increasingly vast and unstructured data sources—organizations seeking to secure AI deployments (and investments) must establish a strong AI governance initiative with data governance at its core.

This article delves into the essentials of AI governance, outlines its importance, examines the challenges involved, and presents best practices to help companies implement a resilient, secure, and ethically sound AI governance framework centered around data.

What is AI Governance?

AI governance encompasses the frameworks, practices, and policies that guide the responsible, safe, and ethical use of AI systems across an organization. Effective AI governance integrates technical elements—data, models, and code—with human oversight for a holistic framework that evolves alongside an organization’s AI initiatives.

Embedding AI governance, along with related data security measures, into organizational practices not only guarantees responsible AI use but also long-term success in an increasingly AI-driven world.

With an AI governance structure rooted in secure data practices, your company can:

Mitigate risks: Ongoing AI risk assessments can proactively identify and address potential threats, such as algorithmic bias, transparency gaps, and potential data leakage; this ensures fairer AI outcomes while minimizing reputational and regulatory risks tied to flawed or opaque AI systems.
Ensure strict adherence: Effective AI governance and compliance policies create clear accountability structures, aligning AI deployments and data use with both internal guidelines and the broader regulatory landscape such as data privacy laws or industry-specific AI standards.
Optimize AI performance: Centralized AI governance provides full visibility into your end-to-end AI deployments一from data sources and engineered feature sets to trained models and inference endpoints; this facilitates faster and more reliable AI innovations while reducing security vulnerabilities.
Foster trust: Ethical AI governance practices, backed by strict data security, reinforce trust by ensuring AI systems are transparent and safe, which is crucial for building confidence among both internal and external stakeholders.

A robust AI governance framework means your organization can safeguard sensitive data, build trust, and responsibly harness AI’s transformative potential, all while maintaining a transparent and aligned approach to AI.

Why Data Governance Is at the Center of AI Governance

Data governance is key to effective AI governance because AI systems require high-quality, secure data to properly function. Accurate, complete, and consistent data is a must for AI performance and the decisions that guide it. Additionally, strong data governance enables organizations to navigate complex regulatory landscapes and mitigate ethical concerns related to bias.

Through a structured data governance framework, organizations can not only achieve compliance but also leverage data as a strategic asset, ultimately leading to more reliable and ethical AI outcomes.

Risks of Not Having a Data-Driven AI Governance Framework

AI systems are inherently complex, non-deterministic, and highly adaptive—characteristics that pose unique challenges for governance.

Many organizations face difficulty blending AI governance with their existing data governance and IT protocols; however, a centralized approach to governance is necessary for comprehensive oversight. Without a data-centric AI governance framework, organizations face risks such as:

Opaque decision-making: Without clear lineage and governance, it becomes difficult to trace and interpret AI decisions, which can lead to unethical, discriminatory, or harmful outcomes.
Data breaches: AI systems rely on large volumes of data, making rigorous data security protocols essential to avoid leaks of sensitive information across an extended attack surface covering both model inputs and outputs.
Regulatory non-compliance: The fast-paced evolution of AI regulations means organizations without a governance framework risk large penalties for non-compliance and potential reputational damage.

For more insights on managing AI and data privacy compliance, see our tips for security leaders.

Implementing AI Governance: A Balancing Act

While centralized, robust AI governance is crucial, implementing it successfully poses significant challenges. Organizations must find a balance between driving innovation and maintaining strict oversight of AI operations.

A primary issue is ensuring that governance processes are both adaptable enough to support AI innovation and stringent enough to uphold data security and regulatory compliance. This balance is difficult to achieve, particularly as AI regulations vary widely across jurisdictions and are frequently updated.

Another key challenge is the demand for continuous monitoring and auditing. Effective governance requires real-time tracking of data usage, model behavior, and compliance adherence, which can add significant operational overhead if not managed carefully.

To address these challenges, organizations need an adaptive governance framework that prioritizes privacy, data security, and ethical responsibility, while also supporting operational efficiency and scalability.

Frameworks & Best Practices for Implementing Data-Driven AI Governance

While there is no universal model for AI governance, your organization can look to established frameworks, such as the AI Act or OECD AI Principles, to create a framework tailored to your own risk tolerance, industry regulations, AI use cases, and culture.

Below we explore key data-driven best practices—relevant across AI use cases—that can best help you structure an effective and secure data-centric AI governance framework.

Adopt a Lifecycle Approach

A lifecycle approach divides oversight into stages. Implementing governance at each stage of the AI lifecycle enables thorough oversight of projects from start to finish following a multi-layered security strategy.

For example, in the development phase, teams can conduct data risk assessments, while ongoing performance monitoring ensures long-term alignment with governance policies and control over data drift.

Prioritize Data Security

Protecting sensitive data is foundational to responsible AI governance. Begin by achieving full visibility into data assets, categorize them by relevance, and then assign risk scores to prioritize security actions.

An advanced data risk assessment combined with data detection and response (DDR) can help you streamline risk scoring and threat mitigation across your entire data catalog, ensuring a strong data security posture.

Adopt a Least Privilege Access Model

Restricting data access based on user roles and responsibilities limits unauthorized access and aligns with a zero-trust security approach. By ensuring that sensitive data is accessible only to those who need it for their work via least privilege, you reduce the risk of data breaches and enhance overall data security.

Establish Data Quality Monitoring

Ongoing data quality checks help maintain data integrity and accuracy, meaning AI systems will be trained on high-quality data sets and serve quality requests.

Implement processes for continuous monitoring of data quality and regularly assess data integrity and accuracy; this will minimize risks associated with poor data quality and improve AI performance by keeping data aligned with governance standards.

Implement AI-Specific Detection and Response Mechanisms

Continuous monitoring of AI systems for anomalies in data patterns or performance is critical for detecting risks before they escalate.

Anomaly detection for AI deployments can alert security teams in real time to unusual access patterns or shifts in model performance. Automated incident response protocols guarantee quick intervention, maintaining AI output integrity and protecting against potential threats.

A data security posture management (DSPM) tool allows you to incorporate continuous monitoring with minimum overhead to facilitate proactive risk management.

Conclusion

AI governance is essential for responsible, secure, and compliant AI deployments. By prioritizing data governance, organizations can effectively manage risks, enhance transparency, and align with ethical standards while maximizing the operational performance of AI.

As AI technology evolves, governance frameworks must be adaptive, ready to address advancements such as generative AI, and capable of complying with new regulations, like the UK GDPR.

To learn how Sentra can streamline your data and AI compliance efforts, explore our guide on data security posture management (DSPM). Or, see Sentra in action today by signing up for a demo.

David Stuart

November 7, 2024

Min Read

Sentra Case Study

Understanding the Value of DSPM in Today’s Cloud Ecosystem

As businesses accelerate their digital growth, the complexity of securing sensitive data in the cloud is growing just as fast. Data moves quickly and threats are evolving even faster; keeping cloud environments secure has become one of the biggest challenges for security teams today.

In The Hacker News’ webinar, Benny Bloch, CISO at Global-e, and David Stuart, Senior Director of Product Marketing at Sentra, discuss the challenges and solutions associated with Data Security Posture Management (DSPM) and how it's reshaping the way organizations approach data protection in the cloud.

The Shift from Traditional IT Environments to the Cloud

Benny highlights how the move from traditional IT environments to the cloud has dramatically changed the security landscape.

"In the past, we knew the boundaries of our systems. We controlled the servers, firewalls, and databases," Benny explains. However, in the cloud, these boundaries no longer exist. Data is now stored on third-party servers, integrated with SaaS solutions, and constantly moved and copied by data scientists and developers. This interconnectedness creates security challenges, as it becomes difficult to control where data resides and how it is accessed. This transition has led many CISOs to feel a loss of control.

As Benny points out, "When using a SaaS solution, the question becomes, is this part of your organization or not? And where do you draw the line in terms of responsibility and accountability?"

The Role of DSPM in Regaining Control

To address this challenge, organizations are turning to DSPM solutions. While Cloud Security Posture Management (CSPM) tools focus on identifying infrastructure misconfigurations and vulnerabilities, they don’t account for the movement and exposure of data across environments. DSPM, on the other hand, is designed to monitor sensitive data itself, regardless of where it resides in the cloud.

David Stuart emphasizes this difference: "CSPM focuses on your infrastructure. It’s great for monitoring cloud configurations, but DSPM tracks the movement and exposure of sensitive data. It ensures that security protections follow the data, wherever it goes."

For Benny, adopting a DSPM solution has been crucial in regaining a sense of control over data security. "Our primary goal is to protect data," he says. "While we have tools to monitor our infrastructure, it’s the data that we care most about. DSPM allows us to see where data moves, how it’s controlled, and where potential exposures lie."

Enhancing the Security Stack with DSPM

One of the biggest advantages of DSPM is its ability to complement existing security tools. For example, Benny points out that DSPM helps him make more informed decisions about where to prioritize resources. "I’m willing to take more risks in environments that don’t hold significant data. If a server has a vulnerability but isn’t connected to sensitive data, I know I have time to patch it."

By using DSPM, organizations can optimize their security stack, ensuring that data remains protected even as it moves across different environments. This level of visibility enables CISOs to focus on the most critical threats while mitigating risks to sensitive data.

A Smooth Integration with Minimal Disruption

Implementing new security tools can be a challenge, but Benny notes that the integration of Sentra’s DSPM solution was one of the smoothest experiences his team has had. "Sentra’s solution is non-intrusive. You provide account details, install a sentinel in your VPC, and you start seeing insights right away," he explains. Unlike other tools that require complex integrations, DSPM offers a connector-less architecture that reduces the need for ongoing maintenance and reconfiguration.

This ease of deployment allows security teams to focus on monitoring and securing data, rather than dealing with the technical challenges of integration.

The Future of Data Security with Sentra’s DSPM

As organizations continue to rely on cloud-based services, the need for comprehensive data security solutions will only grow. DSPM is emerging as a critical component of the security stack, offering the visibility and control that CISOs need to protect their most valuable assets: data.

By integrating DSPM with other security tools like CSPM, organizations can ensure that their cloud environments remain secure, even as data moves across borders and infrastructures. As Benny concludes, "You need an ecosystem of tools that complement each other. DSPM gives you the visibility you need to make informed decisions and protect your data, no matter where it resides."

This shift towards data-centric protection is the future of AI-era security, helping organizations stay ahead of threats and maintain control over their ever-expanding digital environments.

‍

Team Sentra

October 28, 2024

Min Read

Data Security

Spooky Stories of Data Breaches

As Halloween approaches, it’s the perfect time to dive into some of the scariest data breaches of 2024. Just like monsters hiding in haunted houses, cyber threats quietly move through the digital world, waiting to target vulnerable organizations.

The financial impact of cyberattacks is immense. Cybersecurity Ventures estimates global cybercrime will reach $9.5 trillion in 2024 and $10.5 trillion by 2025. Ransomware, the top threat, is projected to cause damages from $42 billion in 2024 to $265 billion by 2031.

If those numbers didn’t scare you, the 2024 Verizon Data Breach Investigations Report highlights that out of 30,458 cyber incidents, 10,626 were confirmed data breaches, with one-third involving ransomware or extortion. Ransomware has been the top threat in 92% of industries and, along with phishing, malware, and DDoS attacks, has caused nearly two-thirds of data breaches in the past three years.

Let's explore some of the most spine-tingling breaches of 2024 and uncover how they could have been avoided.

Major Data Breaches That Shook the Digital World

The Dark Secrets of National Public Data

The latest National Public Data breach is staggering, just this summer, a hacking group claims to have stolen 2.7 billion personal records, potentially affecting nearly everyone in the United States, Canada, and the United Kingdom. This includes American Social Security numbers. They published portions of the stolen data on the dark web, and while experts are still analyzing how accurate and complete the information is (there are only about half a billion people between the US, Canada, and UK), it's likely that most, if not all, social security numbers have been compromised.

The Haunting of AT&T

‍AT&T faced a nightmare when hackers breached their systems, exposing the personal data of 7.6 million current and 65.4 million former customers. The stolen data, including sensitive information like Social Security numbers and account details, surfaced on the dark web in March 2024.

Change Healthcare Faces a Chilling Breach

In February 2024, Change Healthcare fell victim to a massive ransomware attack that exposed the personal information of millions of individuals, with 145 million records exposed. This breach, one of the largest in healthcare history, compromised names, addresses, Social Security numbers, medical records, and other sensitive data. The incident had far-reaching effects on patients, healthcare providers, and insurance companies, prompting many in the healthcare industry to reevaluate their security strategies.

The Nightmare of Ticketmaster

‍Ticketmaster faced a horror of epic proportions when hackers breached their systems, compromising 560 million customer records. This data breach included sensitive details such as payment information, order history, and personal identifiers. The leaked data, offered for sale online, put millions at risk and led to potential federal legal action against their parent company, Live Nation.

How Can Organizations Prevent Data Breaches: Proactive Steps

To mitigate the risk of data breaches, organizations should take proactive steps.

Regularly monitor accounts and credit reports for unusual activity.
Strengthen access controls by minimizing over-privileged users.
Review permissions and encrypt critical data to protect it both at rest and in transit.
Invest in real-time threat detection tools and conduct regular security audits to help identify vulnerabilities and respond quickly to emerging threats.
Implement Data Security Posture Management (DSPM) to detect shadow data and ensure proper data hygiene (i.e. encryption, masking, activity logging, etc.)

These measures, including multi-factor authentication and routine compliance audits, can significantly reduce the risk of breaches and better protect sensitive information.

Best Practices to Secure Your Data

Enough of the scary news, how do we avoid these nightmares?

Organizations can defend themselves starting with Data Security Posture Management (DSPM) tools. By finding and eliminating shadow data, identifying over-privileged users, and monitoring data movement, companies can significantly reduce their risk of facing these digital threats.

Looking at these major breaches, it's clear the stakes have never been higher. Each incident highlights the vulnerabilities we face and the urgent need for strong protection strategies. Learning from these missteps underscores the importance of prioritizing data security.

As technology continues to evolve and regulations grow stricter, it’s vital for businesses to adopt a proactive approach to safeguarding their data. Implementing proper data security measures can play a critical role in protecting sensitive information and minimizing the risk of future breaches.

‍Sentra: The Data Security Platform for the AI era

‍Sentra enables security teams to gain full visibility and control of data, as well as protect against sensitive data breaches across the entire public cloud stack. By discovering where all the sensitive data is, how it's secured, and where it's going, Sentra reduces the 'data attack surface', the sum of all places where sensitive or critical data is stored or traveling to.Sentra’s cloud-native design combines powerful Data Discovery and Classification, DSPM, DAG, and DDR capabilities into a complete Data Security Platform (DSP). With this, Sentra customers achieve enterprise-scale data protection and answer the important questions about their data. Sentra DSP provides a crucial layer of protection distinct from other infrastructure-dependent layers. It allows organizations to scale data protection across multi-clouds to meet enterprise demands and keep pace with ever-evolving business needs. And it does so very efficiently - without creating undue burdens on the personnel who must manage it.