Wiz, Inc.

Alon Schindel

Ami Luttwak

Roy Reznik

Yinon Costica

Amitai Cohen

Barak Sharoni

Yaniv Shaked

George Pisha

Shir Tamari

Udi Reitblat

Ron Cohen

Aviel Fogel

Itay Arbel

Elad Gabay

Mattan Shalev

Liran Moysi

Liron Levin

Maayan Laav

Itay Vanzetti

Daniel Hershko Shemesh

Yarin Miran

Raaz Herzberg

Gal Kozoshnik

Alon Weiss

Itamar Gilad

Guy Rozendorn

A system and method for detecting privilege escalation on a resource deployed in a computing environment is disclosed. The method includes: configuring the resource to deploy thereon a sensor, the sensor configured to listen on a data link layer of the resource for an event; receiving from the sensor a permission-based event based on a first actor, the permission-based event indicating a first permission set of the first actor; querying a database to detect a second permission set of the first actor; detecting that the first permission set includes a permission which is not in the second permission set; determining that the resource is involved in a privilege escalation event in response to detecting that the first permission set includes a permission which is not in the second permission set; and initiating a mitigation action in response to the determined privilege escalation event.

Techniques for utilizing a sensor in detecting privilege escalation

A system and method for iterative cybersecurity remediation based on a digital forensic finding is disclosed. In an embodiment, the method includes detecting a forensic finding, the forensic finding based on a forensic artifact detected on a disk of a resource in a computing environment; generating an inspectable disk based on the disk of the resource; inspecting the inspectable disk for a cybersecurity object based on the forensic artifact; and initiating a remediation action on the disk based on the cybersecurity object detected on the inspectable disk.

Techniques for system feedback in remediating cybersecurity risks

A system and method for generating a contextual cloud risk assessment of a cloud computing environment. The method includes accessing a plurality of cloud assessment policies, wherein a policy including a query executable on a security graph; applying the plurality of cloud assessment policies to the representation of the first cloud computing environment; generating a risk assessment report based on an output generated by applying a policy of the plurality of cloud assessment polices; and initiating a mitigation action based on a cybersecurity risk from the risk assessment report.

System and method for risk monitoring of cloud based computing environments

A system and method improves cloud detection and response by generating a normalized event log from a plurality of cloud computing layers. The method includes receiving a plurality of events, wherein a first event is generated in a first cloud layer of a cloud computing environment provided by a cloud service provider (CSP) and a second event is generated in a second cloud layer of the cloud computing environment; extracting data from each event; generating a normalized event based on the extracted data and further based on a predefined data schema, the predefined schema including a plurality of data fields, at least a portion of which are related to cloud layers; storing the normalized event in a transactional database having stored therein a normalized event log; and applying a rule from a rule engine on the normalized event to detect a cybersecurity threat in the cloud computing environment.

System and method for generating normalized event logs for cloud detection and response in a multi-layered cloud environment

A system and method improves cloud detection and response by generating a normalized event log from a plurality of cloud service providers (CSPs). The method includes receiving a plurality of events, wherein a first event of the plurality of events is generated in a cloud computing environment provided by a first CSP and a second event of the plurality of events is generated in a cloud computing environment provided by a second CSP; extracting data from an event of the plurality of events; generating a normalized event based on the extracted data and a predefined data schema, the predefined data schema including a plurality of data fields; storing the normalized event in a transactional database having stored therein a normalized event log; and applying a rule from a rule engine on a normalized event stored in the transactional database to detect a cybersecurity threat in any of the CSPs.

System and method for threat detection across multiple cloud environments utilizing normalized event logs

A system and method for detecting cloud identity misuse in a cloud computing environment is presented. The method includes: deploying a runtime sensor on a workload in a cloud computing environment; continuously receiving data from the runtime sensor; generating an activity baseline based on the continuously received data, wherein the runtime sensor is configured to detect runtime processes on the workload; detecting an event in a cloud log, the event including an identifier of the workload; associating a runtime process detected by the runtime sensor on the workload with the event detected in the cloud log; and determining that the event is an anomalous event based on the generated activity baseline.

Techniques for detecting cloud identity misuse leveraging runtime context

A system and method for improved endpoint detection and response (EDR) in a cloud computing environment initiates inspection based on data received from a sensor deployed on a workload. The method includes: configuring a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data; detecting a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; and initiating inspection of the resource for the potential cybersecurity threat.

System and method for cybersecurity threat detection utilizing static and runtime data

A system and method for detecting a combined cybersecurity risk for an artificial intelligence (AI) model is presented. The method includes: inspecting a computing environment for an AI model deployed therein; generating a representation of the AI model in a security database, the security database including a representation of the computing environment; detecting a first cybersecurity risk respective of the AI model; inspecting the computing environment for a cybersecurity object; determining that the AI model is exposed to a toxic combination cybersecurity risk based on the detected first cybersecurity risk and the cybersecurity object; and initiating a mitigation action based on the toxic combination cybersecurity risk.

System and method for detecting cybersecurity risks in managed artificial intelligence models

A system and method detect a malware infection path in a compute environment. The method includes detecting a malware object on a first workload in a computing environment including a plurality of workloads, wherein the first workload is represented by a resource node on a security graph, the security graph including an endpoint node representing a resource which is accessible to a public network; generating a potential infection path between the resource node and the endpoint node including at least a second resource node connected to the resource node; inspecting a second workload of the plurality of workloads represented by the second resource node; determining that the potential infection path is a confirmed infection path, in response to detecting the malware on the second workload; and determining that the potential infection path is not an infection path, in response to detecting that the second workload does not include the malware.

Detecting malware infection path in a cloud computing environment utilizing a security graph

A system and method for preventing cloud identity misuse in a cloud computing environment is presented. The method includes: deploying a runtime sensor on a workload in a cloud computing environment; detecting an event in a cloud log, the event including an identifier of the workload; associating a runtime process detected by the runtime sensor on the workload with the event detected in the cloud log; generating an enriched event based on: the detected event, and an identifier of the associated runtime process; applying a policy on the enriched event; and initiating a mitigation action in the cloud computing environment based on a result of applying the policy on the enriched event.

Techniques for preventing cloud identity misuse leveraging runtime context

A system and method for associating an event in a cloud computing log to a process running on a workload is presented. The method includes: configuring a workload deployed in a cloud computing environment to deploy thereon a sensor, the sensor configured to detect a runtime process on the workload, the runtime process utilizing an identity; detecting in a log of the cloud computing environment an event based on an identifier of the workload, the log including a plurality of events; inspecting a code object for a cybersecurity object, the code object utilized in deploying the workload in the cloud computing environment; associating the runtime process with the event based on an identifier of the workload and the cybersecurity object, wherein the cybersecurity object indicates the identity; and generating an enriched log including an identifier of the runtime process associated with the event and the cybersecurity object.

Techniques for cybersecurity investigation of cloud entity misuse leveraging runtime context

A system and method for detecting cybersecurity risk on a resource in a computing environment utilizes static analysis of a cloned resource and runtime data from the live resource. The method includes: configuring a resource deployed in a computing environment to deploy thereon a sensor, the sensor configured to detect runtime data; detecting runtime data from the sensor of the resource; generating an inspectable disk based on an original disk of the resource; initiating inspection based on the detected runtime data for a cybersecurity object on the inspectable disk; detecting the cybersecurity object on an inspectable disk; and initiating a mitigation action on the resource.

Techniques for cybersecurity inspection based on runtime data and static analysis from cloned resources

A system and method for identifying cloud identity misuse based on run-time time data and static analysis is presented. The method includes: detecting a workload in a cloud computing environment; configuring the workload to deploy a sensor configured to detect data respective of a runtime process executed on the workload; detecting an original disk associated with the workload; generating an inspectable disk based on the original disk; inspecting the inspectable disk for a cybersecurity object; detecting in a log of the cloud computing environment an event based on an identifier of the workload; inspecting a code object for an identity object, the code object utilized in deploying the workload in the cloud computing environment; associating the runtime process with the event based on: an identifier of the workload, the identity object, and the cybersecurity object; and generating an enriched log including an identifier of the runtime process.

Techniques for detecting cloud identity misuse based on runtime context and static analysis

A system and method detecting an artificial intelligence (AI) pipeline in a cloud computing environment. The method includes: inspecting a cloud computing environment for an AI pipeline component; detecting a connection between a first AI pipeline component and a second AI pipeline component; generating a representation of each of: the first AI pipeline component, the second AI pipeline component, and the connection, in a security database; and generating an AI pipeline based on the generated representations.

Techniques for detecting AI pipelines in cloud computing environments

A system and method for providing cybersecurity incident response utilizing a large language model. The method includes: mapping a received incident input into a scenario of a plurality of scenarios, each scenario including a plurality of sub-scenarios; generating a query based on the received incident input and a selection of a sub-scenario of the plurality of sub-scenarios; executing the query on a security database, the security database including a representation of the computing environment; and initiating a mitigation action based on a result of the executed query.

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Tel Aviv, Israel

Alon Schindel