What is Data Detection and Response (DDR)?
While the cloud leads to more innovative, streamlined businesses, it also brings new data security challenges. When data was primarily stored within on-premise infrastructure, it took layers of permissions and often some level of physical proximity to access proprietary data. By contrast, virtually any staff member can access your cloud environment when they log into the system remotely. While this widespread access to data is a good thing, it also means that it’s easy for staff to move, change, or copy data — including sensitive assets such as customer data, developer secrets, and other proprietary information. Businesses need a data security solution built for cloud infrastructure to protect this constantly changing environment.
Data Detection & Response (DDR) is a cloud-native tool that enables organizations to discover and respond to threats affecting their data in real time. When used with Data Security Posture Management (DSPM) and Data Access Governance (DAG), it effectively identifies security threats and prevents sensitive data exfiltration.
We’ll dive deeper into DDR in this guide, covering:
- How DDR works
- The relationship between DDR, DSPM, and DAG
- How DDR strengthens adjacent efforts like cloud security and compliance
- Which components are included in a typical DDR solution
- What key features your team should prioritize when choosing a solution
The Role of Data Detection and Response (DDR) in Cybersecurity
Data detection and response is a cloud-native approach to data loss prevention (DLP). While DLP used to be an essential component to most cybersecurity programs, today’s cloud and hybrid organizations either steer away from using it altogether or search for ways to supplement it and address unmet exposures.
Legacy DLP focuses on preventing on-prem data leakage, making it suboptimal for cloud-native environments and managed services. It’s often manually configured and administered, leading to inefficiencies, alert fatigue, and reduced risk detection accuracy due to high false positives. Many businesses also find it frustrating when traditional DLP automatically blocks access to applications and services in IaaS and PaaS environments.
Data Detection and Response (DDR) is the next generation of cybersecurity for cloud environments. It provides several functions for securing cloud data, including:
- Real-time monitoring. DDR automatically analyzes log events and flags signs of emerging threats within your cloud environment.
- Data movement detection. DDR can instantaneously alert you if your sensitive data gets moved or copied.
- Alerting on suspicious activity and anomalies. DDR automatically prioritizes the severity of suspicious activities, leveraging contextual understanding of location, user, sensitivity, etc., and then alerts the security team based on this prioritization.
- Data policy enforcement. DDR detects if a user violates an industry compliance framework or regulatory standards such as sovereignty or privacy adherence.
In addition, many teams leverage the findings from their DDR solution to better understand their applications’ data flows, pipelines, and access permissions. This information on data architecture and usage is invaluable for conducting risk assessments and speeding up incident resolution.
DDR can identify data-centric threats that might go unnoticed by other monitoring tools such as XDR/EDR/CDR. A few examples include:
- Users downloading sensitive data types that they usually don’t access
- A ransomware attack in which business-critical data is encrypted or tampered with
- Users or applications who gain access to sensitive data via a privilege escalation
- A third-party application tampering or poisoning an LLM training dataset
- A supply chain attack detection in which a compromised or malicious third-party app is exfiltrating sensitive data from your cloud environment
- Anonymous users accessing sensitive data from an obfuscated site (e.g., from a Tor exit node)
- Credentials extraction of high-impact keys that have access to sensitive data
The Power of DDR, DSPM, and DAG (Better Together)
Many organizations also lean on DSPM and DAG solutions to fully understand their data environment and ensure that their DDR covers every corner of their business.
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DDR and Data Security Posture Management (DSPM) complement each other. DDR is a primarily reactive approach; it detects and responds to external and internal threats in real-time. DSPM, on the other hand, offers a proactive approach by detecting and remediating data security risks, such as misconfigurations, mislocated data, and excessive permissions.
The deep level of context that DSPM produces enhances the ability of DDR to detect obscure data threats - often by unsuspected authorized insiders and ecosystem partners or compromised identities.
Data Access Governance (DAG) solutions also increase the effectiveness of DDR tools by flagging high-risk identities (based on behavior) and proactively preventing access control risks. DAG discovers and remediates access issues such as excessive permissions, unauthorized access, inactive or unused identities and API keys, and improper service and user provisioning/deprovisioning. DAG enables organizations to achieve least privilege access in their existing environment, while DDR enforces appropriate access permissions as future changes happen.
By implementing all three technologies, businesses can simultaneously solve existing security risks and watch out for emerging ones.
Data Breach Response and the Power of DDR
According to the most recent IBM Cost of a Data Breach report, 82% of reported data breaches involved data stored in cloud environments. Also, according to the report, the cost of a data breach continues to rise each year. The cost reached an all-time high of $4.45 million that year — a 2.3% increase from the previous year. Plus, this monetary value doesn’t even account for reputation loss.
The same study offers several suggestions for lowering the risk of a data breach, including the recommendation to adopt data activity-monitoring solutions. These solutions “can help ensure proper controls are in place while actively enforcing these policies — such as early detection of suspicious activity and blocking real-time threats to critical data stores.”
In most cases, remediating a data breach is a race against time. The report uncovered that when the attacker publicly announces the data breach, it costs organizations nearly $1 million more than if they had discovered it through internal detection.
By monitoring your system 24/7 and alerting on any suspicious activity, DDR can help support your data security strategy and fulfill this recommendation. Alongside DSPM and DAG, it enables your team to respond to a data breach as rapidly as possible — before it impacts your business.
DDR's Role in Mitigating Data Privacy Violations
In addition to preventing data breaches in real-time, DDR, along with DSPM, helps organizations comply with data privacy regulations. Meeting compliance regulations is paramount for today’s businesses as it mitigates security risks, helps your business avoid penalties, establishes trust with customers and stakeholders, and differentiates you from the competition.
However, most cloud environments’ setups make it challenging to align with data regulations. The number of users and variety of data stores within a given organization’s cloud environment can blur the boundaries between sensitive and non-sensitive data. Plus, most organizations face limited visibility and a lack of control over sensitive assets in the cloud.
DDR and DSPM answer these challenges by monitoring your cloud environment for data policy violations and flagging them as soon as they occur. This proactive approach to compliance helps your organization stay audit-ready and minimizes legal consequences and fines.
DDR’s Role in Cloud Security: A Crucial Piece of the Puzzle
DDR protects your cloud environment alongside other technologies such as CNAPP and CSPM. It complements the infrastructure, network, and application security efforts these cloud infrastructure security tools offer.
For example, a CSPM solution might flag that a public S3 bucket contains sensitive information and suggest making it private. However, if this bucket contains sensitive data that is already masked or encrypted, there’s no need to take the time and effort to change its configuration.
It’s also possible to accidentally expose sensitive data in the cloud when moving it from a well-protected production data lake or data warehouse to a less protected data store. It’s common for cloud data to get moved around this way for BI or development-testing purposes. Understanding the full context of all sensitive data, such as its location, environment, account, user, and usage, enables the correct policy/posture to be applied.
DDR, along with DSPM, fills in this missing data-centric context by…
- Differentiating security events that contain high-risk sensitive data
- Correlating high-risk sensitive data sources with potential threats
- Prioritizing risk based on data security posture and context
- Decreasing alert fatigue and increasing productivity with accurate, automated risk and threat prioritization
The Four Components of Data Detection and Response Solutions
But how exactly do DDR solutions offer all of the above? We can break down the functionality of a typical DDR solution into four categories:
Monitoring
DDR monitors your sensitive assets using the logs from your cloud providers. AWS CloudTrail is an example of one of these cloud logging services. DDR takes info from this type of service and then parses the events on these logs to find any unusual activity or policy violations.
To configure their DDR solution and ensure it covers all of these sensitive assets, many organizations will first leverage DSPM to identify all known and unknown sensitive data across their environment. Then, they can utilize the contextual information on data location, sensitivity, etc., to optimally tune the DDR solution, enabling it to monitor and protect the most at-risk assets. This DSPM/DDR pairing helps reduce the ‘noise’ that other types of log monitoring tend to generate.
Detection
DDR’s functionality relies on robust threat detection and anomaly identification. DDR typically focuses these efforts on the following types of events:
- Suspicious third-party or insider access
- Data exfiltration
- Accidental or unauthorized data leakage
- Signs of weakening defense, such as escalated access privileges, encryption level, sensitivity classification, or data ownership
DDR also looks for specific data policy violations set by the team or based on compliance regulations.
Alert
Next, DDR prioritizes each detected incident and then alerts the right personnel on the details and severity of each event. Rather than basing the severity level on general vulnerability categories, it uses data context to dig into the actual risk level to the business.
For example, a ‘severe’ endpoint vulnerability might not actually be a legitimate concern if it doesn’t contain any sensitive data. In contrast, a less severe vulnerability related to sensitive assets could pose a critical threat to the business. By considering all of these factors, DDR reduces alert fatigue and keeps teams focused on which actions matter most.
Response
Lastly, DDR enables teams to respond to these alerts. It offers automated workflows for remediation and practical tips for manual activities if needed. A good DDR solution will provide straightforward alerts that look something like this:
‘Severe Data Vulnerability: Company source code has been found in the following unsecured data store:____. This vulnerability can be remediated by taking the following steps: ___’.
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In the case of a successful breach, DDR provides guidance to initiate remediation actions and contextual information, such as an event timeline, to aid and speed up post-incident analysis.
Key Considerations for Selecting a DDR Solution
As you’ve probably already seen, there are many DDR solutions to choose from. So, where should you start in choosing the best option? Here are some criteria to consider:
- Security workflow integrations that enable your team to directly feed alert context to speed resolution and automatically route issues to the appropriate teams
- Close ties with DSPM and DAG that provide detailed context to improve monitoring and facilitate a complete agile data security approach
- Data profiling and metadata analysis that boost security policy formation with information on data attributes, schema, and relationships
- Behavioral analytics and anomaly detection algorithms that enable the solution to detect deviations from your business’s normal patterns
- Automated incident response orchestration (and/or integrations to third-party orchestration solutions) that automates remediation actions such as isolating compromised systems, quarantining data, or initiating remediation workflows automatically
- Seamless deployment, with an agentless, API-only setup that shows quick time to value and doesn’t impact your workloads or databases’ performance
Sentra’s Agile Data Security Platform
Sentra offers data detection and response capabilities that enable businesses to secure their most valuable asset — their data — no matter where or how it travels.
Our DDR tool capabilities include:
- Sensitive data exfiltration detection
- Suspicious sensitive data access detection (insider or 3rd party)
- Compliance monitoring of data access
- Detection of weakening defenses around sensitive data
- Ransomware prevention
- Sensitive data loss prevention
- Detection of zero trust access and privileges violations
In addition, we provide support for every stage of the data lifecycle, uniting DSPM, DDR, and DAG functions in a single solution. With this multi-dimension approach to cloud data security, your business can…
- Protect sensitive shadow data by identifying and removing critical shadow data (PCI, PII, PHI, source code, etc.) from improper locations
- Reduce the data attack surface to keep your cloud environment protected
- Match your security posture to your data, following it across your environment
- Ensure that sensitive data always has proper access controls wherever it lands
- Automate compliance with custom and pre-built policies that travel with your data
- Leverage a single source of truth (e.g., up-to-date and accurate data catalog) to break silos between security, operations, business intelligence, and engineering teams
Contact us today to learn more about our approach to cloud data security, including analyzing and responding to emerging threats with DDR.
What is Data Detection and Response (DDR)?
Data Detection & Response (DDR) is a cloud-native tool that enables organizations to discover and respond to threats affecting their data in real time. When used with Data Security Posture Management (DSPM) and Data Access Governance (DAG), it effectively identifies security threats and prevents sensitive data exfiltration.
What is the difference between DDR and DSPM?
DDR focuses on identifying and mitigating data security threats, actively monitoring and securing sensitive data, minimizing noise, and preventing alert fatigue. In contrast, DSPM implements security measures to protect data from unauthorized access, ensuring confidentiality, integrity, and availability. Together, they form a comprehensive approach to data security.
What strategies can be used to secure data in a multi-cloud environment?
Strategies to secure data in a multi-cloud environment include using strong authentication and access controls, encrypting data both in transit and at rest, and regularly auditing and monitoring cloud providers' security practices.
What role does continuous monitoring play in maintaining cloud data security?
Continuous monitoring is essential for maintaining cloud data security as it enables organizations to detect and remediate drifts of data security posture, such as when sensitive data is copied to a new data store with no security controls. In addition, there is also a need to detect and respond to data security threats related to data access in real-time, reducing the risk of data loss and ensuring compliance.
Is DSPM part of Cnapp?
No, DSPM (Data Security Posture Management) is not a component of Cnapp (Cloud Native Application Protection Platform). While Cnapp focuses on securing cloud-native applications, DSPM is dedicated to managing and securing cloud data. However, integrating both solutions can bolster overall cloud security posture.
How can organizations maintain consistency in security policies across multi-cloud and hybrid infrastructures?
Organizations can maintain consistency in security policies across multi-cloud and hybrid infrastructures by using standardized security protocols, implementing a central management system, and conducting regular security audits.
How do I secure my data in the cloud?
Cloud computing can be a secure way to store data, but it's important to take steps to protect your data. Here are some tips:
- Choose a Reputable Cloud Provider: Do your research and choose a provider with a strong security track record. Look for a provider that offers a variety of security features.
- Use Strong Passwords and Two-Factor Authentication: Use passwords that are at least 12 characters long and include a mix of upper and lowercase letters, numbers, and symbols. Two-factor authentication adds an extra layer of security by requiring you to enter a code from your phone in addition to your password.
- Encrypt Your Data: Encrypt your data using a strong encryption algorithm. This will make it much more difficult for unauthorized users to access your data, even if they gain access to your cloud account.
- Monitor Your Cloud Usage for Suspicious Activity: Use the tools that your cloud provider offers to monitor your cloud usage for suspicious activity. This could include unauthorized access, unusual file transfers, or changes to your account settings.
- Keep Your Software Up to Date: Cloud providers regularly release security updates to their software. It is important to keep your software up to date to ensure that you are protected from the latest security threats.
By following these tips, you can keep your data secure in the cloud. However, it is important to remember that no security measure is perfect. There is always a risk of data breaches, even in the cloud. By taking the necessary security precautions, you can mitigate this risk and protect your data.
Which aspect is the most important for cloud security?
Data protection is the most important aspect of cloud security. This means ensuring that data is encrypted and protected from unauthorized access, modification, or deletion. Without proper data protection, all other security measures are essentially useless.
Here are some of the reasons why data protection is so important:
- Data is the lifeblood of many businesses. Without access to their data, businesses can be crippled.
- Data breaches can damage a business's reputation and lead to financial losses.
- Data can be used to commit identity theft, fraud, and other crimes.
- Data can be used to create counterfeit products and services.
There are a number of ways to protect data in the cloud, including:
- Utilize DSPM: Use DSPM (Data Security Posture Management) to continuously assess and enhance your data security measures in the cloud, ensuring robust protection.
- Encrypt Data: Make it unreadable without a key.
- Restrict Access: Use passwords, two-factor authentication, and other methods.
- Use DLP: Identify and prevent unauthorized disclosure of sensitive data.
- Monitor Usage: Spot suspicious activity.
By taking a comprehensive approach to cloud security, businesses can help protect their data and applications from attack.
Is storing data in the cloud safer?
Cloud storage, when approached thoughtfully, can offer a secure platform for data. Reputable cloud providers typically invest in robust security measures, including encryption, regular security audits, and advanced access controls. However, complete safety depends on a shared responsibility model. While cloud providers fortify the underlying infrastructure, users play a vital role by implementing strong authentication, encryption for sensitive data, and regular monitoring of suspicious activities within their accounts. Comprehending the nuances of cloud security and adopting security best practices collectively create a solid defense against potential threats. Ultimately, data safety in the cloud is a collaborative effort between providers and users, relying on their combined commitment to security measures.
Here are some additional points to consider:
- The cloud provider's security track record.
- The cloud provider's security features.
- The organization's security measures.
- The sensitivity of the data.
Ultimately, the decision of whether or not to store data in the cloud is a risk assessment. Organizations should weigh the risks and benefits of each option and choose the one that best meets their needs.
What’s the difference between cloud security and data security?
Differentiating between cloud security and data security clarifies their distinct roles in the cybersecurity landscape.
Cloud Security:
- Focuses on safeguarding the entire cloud infrastructure, including networks, servers, and services.
- Aims to protect against external threats, manage user access, and ensure the overall integrity of the cloud environment.
Data Security:
- Primarily concerns the confidentiality, integrity, and availability of stored data within the cloud.
- Encompasses encryption, access controls, data classification, and measures to prevent unauthorized access or tampering.
While interconnected, recognizing their specific scopes allows organizations to create a comprehensive security framework that addresses both the protection of the cloud infrastructure and the data it hosts.
What’s the most common type of cloud security threat?
Differentiating between cloud security and data security clarifies their distinct roles in the cybersecurity landscape.
Cloud Security:
- Focuses on safeguarding the entire cloud infrastructure, including networks, servers, and services.
- Aims to protect against external threats, manage user access, and ensure the overall integrity of the cloud environment.
Data Security:
- Primarily concerns the confidentiality, integrity, and availability of stored data within the cloud.
- Encompasses encryption, access controls, data classification, and measures to prevent unauthorized access or tampering.
While interconnected, recognizing their specific scopes allows organizations to create a comprehensive security framework that addresses both the protection of the cloud infrastructure and the data it hosts.
Alex has nearly a decade of extensive programming experience in the areas of Computer Networks and Cyber Security, with emphasis on Python, Go, C++ programming, software design, research and development of network protocols. He specializes in back-end development, and is currently the Data Engineering Team Lead at Sentra.
