The ability of an actor to remain undiscovered or obfuscating its doings when driving a malicious campaign usually affects the gains of such campaigns. These gains can be measured in different items such as time to allow completion of operations (exfiltration, movement of compromised data), ability to remain operative before take down notices are issued, or ability to obtain gains based on for-profit driven crimeware (DDoS for hire, Crypto mining).

Edge computing is likely the most interesting section of the broader world of IoT. If IoT is about connecting all the devices to the Internet, edge computing is about giving more processing power to devices at the edge. Edge computing views these edge devices as mini clouds or mini data centers. They each have their own mini servers, mini networking, mini storage, apps running on top of this infrastructure, and endpoint devices.

Our digital surface is expanding rapidly and threats are becoming more sophisticated day by day. This is putting enormous strain on security teams, which have already been stretched to the limits. Nonetheless, organizations are skeptical of relieving this cybersecurity strain with AI and automation. Why does this situation persist when it’s simply against the logic?

A type of credential reuse attack known as credential stuffing has been recently observed in higher numbers towards industry verticals. Credential stuffing is the process of automated probing of and access to online services using credentials usually coming from data breaches, or bought in the criminal underground.

An ever-increasing number of organizations are working in the cloud. It depends on their business model what cloud delivery model they use. The three most common deployment models for cloud services are software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-Service (IaaS). Here, we explain the differences between these cloud delivery models and what you need to consider when choosing the right model for your organization.

At Sumo Logic, we manage petabytes of unstructured log data as part of our core log search and analytics offering. Multiple terabytes of data are indexed every day and stored persistently in AWS S3. When a query is executed against this data via UI, API, scheduled search or pre-installed apps, the indexed files are retrieved from S3 and cached in a custom read-through cache for these AWS S3 objects. For the most part, the caching scheme for S3 objects works reasonably well.

With the advent of microservices, technologies like Docker, Kubernetes and services like Cloud Computing, have showcased the broader need for observability. Collecting valuable information about the communication endpoints and how they propagate through the discrete components of the application stack is the key to understanding when, why and what happens in case of failure.