Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, data from public sites, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across devices and sensors in a machine-to-machine (M2M) or Internet of things (IOT) environment. For example, a device is assigned a risk score which is based on baseline factors such as expected network packets between two devices, normal network packets, access to critical devices, authorized access requests from one device to another device, normal communications to a device, and the critical ports of a device; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, and anomalies from normal behavior patterns. Techniques encompass risk management by computing a risk score in a timely fashion in accordance with an architecture that enables achieving the required scaling necessitated by the huge number of devices in the machine-to-machine (M2M) or Internet of things (IOT) environment.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across entities such as user identities and other objects. For example, an identity is assigned a risk score which is based on baseline factors such as HR attributes, such as training and screening status; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, as well as anomalies from normal behavior patterns. Techniques herein encompass the management of a risk profile (“behavior profile”) for each entity, e.g. identity, and maintains a risk score that is correlated with behavior, e.g. an individual's behavior, to track anomalies or irregularities in every day routines of the entity, e.g. individual.

Abstract: Techniques are provided that produce a risk profile consisting of a risk score and trends of risk scores across entities such as user identities and other objects. For example, an identity is assigned a risk score which is based on baseline factors such as HR attributes, such as training and screening status; access to and conflicts across physical, logical, and operational systems; historical and current usage of these systems, as well as anomalies from normal behavior patterns. Techniques herein encompass the management of a risk profile (“behavior profile”) for each entity, e.g. identity, and maintains a risk score that is correlated with behavior, e.g. an individual's behavior, to track anomalies or irregularities in every day routines of the entity, e.g. individual.

Abstract: In one embodiment, a method includes determining a plurality of event types where each event type being stored in association with event metadata. One or more events are received and an event type in the plurality of event types for each received event is determined. The method determines event metadata for each determined event type and event data from the event based on the event metadata. One or more control data objects are determined stored in association with the determined event type; one or more rules stored in associated with each of the control data objects is determined where the rule comprises one or more criteria being evaluated against one of the data values of said event; and evaluating the event data with the criterion to determine if each of the events is non-compliant or compliant with a low-level system requirement of said rule.

Abstract: In one embodiment, a method includes determining a plurality of event types where each event type associated with event metadata. An event is received and an event type in the plurality of event types for the received event is determined. The method determines event metadata for the determined event type and event data from the event based on the event metadata. A rule associated with the event type is determined where the rule includes a criterion based on a regulatory compliance issue. A computing device evaluates the event data with the criterion to determine if the event is non-compliant or compliant with the regulatory compliance issue.