An Incident Response Framework for Cloud Data Security

Yotam Ben-EzraYotam Ben-Ezra
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An Incident Response Framework for Cloud Data Security

How do you respond to a security incident? In some cases, the answer might be ‘block first, ask questions later’. In the centralized IT infrastructure that was common a decade ago, security teams had tools that could help them identify suspicious behavior and promptly block the resource or endpoint.  But when it comes to cloud data - which has become the lifeblood of the modern enterprise - things aren’t that simple. Business-critical data is mired in tangled dependencies between pipelines and complicated environments, making it much harder to reach for the kill switch. 

Below we explain some of the key challenges of incident response in cloud environments, and suggest best practices for security teams to prevent catastrophic breach events without disrupting data-driven operations.

The challenges of effective incident response planning for cloud data

Security teams need to keep the organization and its customers safe from an ever-evolving threat landscape, while minimizing operational disruption. In the context of data, this is challenging to achieve due to the complexities of modern data stacks as well as the central role that data plays in business operations. In practice, there are three key questions that business grapple with:

1. Who takes action? (Who owns the data?)

Data ownership is an elusive concept in the age of data democratization. The same datasets often have multiple uses across departments: Developers, analysts, business units, and IT teams; and the team producing the data isn’t necessarily the one that relies on it the most. Data sources, pipelines, and permission schemes form a complex web of interdependencies. Even when there are nominal data owners, they may not understand the downstream impacts of removing access or making a change.

This makes finding and involving the right people an essential part of remediating a data security incident. The technical team that has the ability to execute changes (such as removing permissions to a database) is only part of the equation. Security teams need to quickly identify the relevant stakeholders and get their input to prevent unintended consequences.

2. Which incidents take priority?

Separating signal from noise is one of the main problems in cloud security. Multi-cloud environments and an abundance of logging and monitoring services mean that teams are constantly inundated with alerts. Resources are finite; security teams need to focus on the incidents that truly matter.

Security teams need a way to escalate issues with major compliance or data security implications, while leaving lower-priority incidents to be addressed through standard workflows.  Understanding context is key - including the sensitivity of the data (e.g. whether it contains PII or developer secrets), the types of workloads that might be disrupted, and potential business impact. 

3. How do you automate without risking production?

Automated incident response flows were part of traditional DLP tooling, allowing security or IT to block suspicious behavior based on predefined rules. For the reasons we detailed above, this is usually not applicable to cloud data processes. The damage of shutting down a production database is usually too significant, and most businesses would not be comfortable to do so through a fully automated action. 

However, businesses are also wary of fully manual processes predicated on people responding to notifications. Hence they are looking for a middle ground that will allow them to automate some parts of their incident response flows – such as collecting information, checking data ownership, or identifying relevant misconfiguration – while leaving the final decision in human hands. The exact level of automation applied will always be a middle ground.

An Effective Incident Response Framework based on DSPM, DDR, and Cloud DLP

The challenges we outline above are not insurmountable, but they do require organizations to evolve their approach to incident response. The prerequisites for an effective incident response program are context into the data being monitored, ability to identify the data owner, and workflows that address real time risk as well as misconfigurations. We expand on each below.

Stage 1: Advance Preparation

To effectively prepare for and respond to cloud data incidents, organizations need to lay the groundwork with the following:

  • Inventory: ​​Organizations need visibility into their sensitive data in order to identify incidents, prioritize, and respond effectively. This requires creating and maintaining an inventory of data assets across cloud services, including classification of datasets based on attributes like personal data, financial data, intellectual property, etc. – which allows organizations to assess compliance and security risks when an incident occurs, and determine the right response. The inventory should track which cloud services hold sensitive data, who has access, and how it flows between systems.
  • Ownership: Security teams need to identify who owns each sensitive data asset, and who owns the associated risks. As we’ve highlighted above, this can be very difficult to achieve when data is shared and used across multiple business units. A specific resource can fall under the purview of application teams (code and OLTP databases), IT and DevOps (policies and infrastructure), or security teams (security infrastructure, SSO).
  • Integration: Security tools should be tightly integrated with cloud services and infrastructure. This allows pulling context on users, data, and environments to feed into automated investigation and response workflows. Integration should provide visibility into access patterns, data flows between services, and mapping of technical controls like encryption, as well as SIEM / SOAR systems, ticketing platforms, and CSPM tools.
  • Procedural definition: ​​This includes classifying incident severity, specifying escalation paths, delineating stakeholder responsibilities, and detailing the steps for investigation, remediation, and communication. Well-defined procedures allow for smooth coordination between security, IT, and business teams during high-stress incidents. They also provide guidance on the appropriate response based on incident type, data sensitivity, and potential business impact.

Stage 2: Risk Mitigation

The following steps should be taken to reduce the risk and potential damage caused by an incident:

  • Prioritization: Security teams should prioritize incidents based on potential impact and level of risk. This requires understanding the sensitivity of affected data based on previous classification efforts and the level of risk, as well as which applications, workflows, and teams may be disrupted. Incidents involving large volumes of highly sensitive data, or critical production systems, should be escalated and addressed first. 
  • Remediation and validation workflows: Security teams should execute remediation via standardized predefined workflows. This includes automatically opening tickets in ITSM systems to track the incident response and document actions taken. 

Stage 3: Containment and Remediation

Incident response needs to encompass two types of risks: those stemming from posture/configuration issues versus real-time threats, which tend to have a stronger behavioral aspect (someone is doing something that they shouldn’t be doing). Configuration-based risk could include improper encryption policies, overly permissive access controls, or backup misconfigurations, and is typically handled by DSPM tooling.

Real-time risks are immediate threats, such as an unauthorized user accessing sensitive data, abnormal data transfer activity, or severe compliance violations such as customer credit cards being replicated into non-compliant environments. These incidents require rapid triage and containment by security teams to prevent damage. When real-time incidents occur, the priority is to block suspicious access or activity first, and ask questions later. We might want to revoke access to compromised accounts, block users displaying anomalous behaviors, quarantine impacted assets, etc.

When a breach or violation is detected, the following steps should be applied in order to mitigate any potential harms:

  • Triage: Once a cloud data incident is identified, triage processes will focus on validating that the access is indeed unauthorized and determining whether the actor is malicious or harmful. This might involve collecting additional information from threat intelligence systems, validating the actor identity, or identifying the impacted processes. Once the source of the problem and the potential impact are known, a mitigation pathway can be chosen.
  • Containment: Containment actions can include suspending compromised user accounts, stopping affected workloads, restricting network access, and isolating affected cloud resources. The goal is to limit damage and prevent escalation while long-term remediation steps are decided. Containment should be as targeted as possible to avoid unnecessary business disruption.
  • Remediation and validation:  Misconfigurations and compliance issues are addressed by data teams, application teams, IT, or security. After executing approved remediation steps, security tools should re-scan affected data to validate that risks have been removed. In the case of compliance violations, validation can also help to provide evidence to auditors that issues were addressed. Containment actions can include suspending compromised user accounts, stopping affected workloads, restricting network access, and isolating affected cloud resources. The goal is to limit damage and prevent escalation while long-term remediation steps are decided. Containment should be as targeted as possible to avoid unnecessary business disruption.

Using Dig Security for Cloud Remediation

Factors like compliance, data classification, and overall risk posture should drive prioritization. Dig’s Data security posture management capabilities (DSPM) help organizations understand which sensitive data they store and where they store it - across S3 buckets, databases, virtual machines, SaaS, and shared folders. 

Once sensitive data has been identified, it needs to be contextualized from a security and business perspective. Monitoring data flows and lineage can help identify the source of the data and which dependencies will be impacted by a change, while data access governance can help us understand the full scope of access permissions and which ones are being used in practice.

Dig’s unique DDR capabilities allow it to address both real-time threats as well as configuration-based issues - removing the need for managing multiple posture management and DLP tools. However, these flows would be handled differently - as can be seen in the diagram below (which we have borrowed and adopted from one of our customers):

continuous data monitoring

Towards Better Cloud Incident Response

Sensitive data remains the most coveted asset for hackers, ransomware, and theft. Security and compliance incidents are par the course and will continue being a problem for enterprises for the foreseeable future. By adopting modern approaches and solutions to incident response, businesses can provide effective remediation, prioritize sensitive assets and high-risk scenarios, and prevent incidents from spiraling out of control.

Learn more Dig Security

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