When an incident occurs, investigators face a daunting set of challenges. Data is often scattered across multiple platforms—AWS, Azure, GCP, on-premises storage, and a host of managed services. Add in ephemeral resources like containers and serverless functions, and the complexity grows exponentially. Pulling together a coherent picture of what happened, when it happened, and who was involved can feel like searching for a needle in a haystack—especially when time is of the essence.
The Cado platform is purpose-built to streamline this journey from raw data capture to meaningful analysis. By automating the tedious and time-consuming tasks of data gathering, correlation, and prioritization, Cado empowers security teams to focus on what truly matters: understanding the incident and mitigating the threat.
Traditional cloud forensic investigations often require manual effort to gather snapshots, logs, and memory dumps. Analysts might need to log into multiple consoles, run scripts to copy data to secure storage or manage complex workflows to ensure integrity and chain of custody. Cado eliminates much of this heavy lifting by providing automated data capture across a range of sources.
With Cado, investigators can quickly acquire full disk images (including E01 formats), memory captures, and logs without the need for specialized scripts or extensive manual handling. For example, the platform supports:
This automated, push-button approach to data capture drastically reduces the time required to initiate an investigation. Instead of spending hours gathering evidence, analysts can begin examining it in minutes—accelerating the entire incident response process.
The Cado timeline
One of the most significant advantages Cado brings is its ability to unify multiple data sources into a single, coherent view. As organizations adopt multi-cloud strategies, it’s not uncommon for an attacker’s footprint to cross from AWS to Azure, or for critical logs to be found in GCP Storage while related activity is recorded in AWS CloudWatch. Navigating these diverse interfaces and formats can consume precious resources and increase the risk of missing key evidence.
Cado collects and normalizes data from all these environments—public cloud, private cloud, on-premises infrastructure, and beyond—providing a single pane of glass for investigators. This holistic perspective enables analysts to:
With Cado, gaining insight into the full scope of an incident becomes simpler and more intuitive.
As modern applications shift toward microservices architectures, the need for forensics extends beyond traditional virtual machines and into containers, Kubernetes clusters, and serverless functions. These ephemeral resources can vanish quickly, making timely acquisition and analysis even more critical.
Cado is built with modern architecture in mind. Its support for containers and serverless environments allows teams to capture forensic data from short-lived instances before they disappear. By automating this process, Cado ensures that crucial evidence is not lost—enabling thorough investigations, even in the most dynamic and transient cloud-native landscapes.
Cado Insights
Capturing data is only half the battle. Once the evidence is collected, analysts must sift through it to identify indicators of compromise, piece together event timelines, and discover the root cause of the incident. Cado leverages AI-driven analytics to streamline this step. Its algorithms correlate events, highlight suspicious artifacts, and surface key insights automatically.
Instead of manually parsing log files or assembling a timeline from memory snapshots, analysts can immediately review a curated summary that shows what happened, when it happened, and its potential impact. This automated intelligence doesn’t just save time—it helps ensure that no critical clue slips through the cracks.
By bringing together automated data capture, unified visibility, and AI-enhanced analysis, Cado transforms the cloud investigation process. Security teams no longer need to wrestle with manual workflows, chase ephemeral data sources, or waste valuable hours normalizing logs from different platforms. Instead, they can dive straight into understanding the threat and taking decisive action.