Artificial Intelligence in Risk Management

According to risk management expert — Rowan Relton, there are many potential benefits of machine learning and AI (artificial intelligence) for risk management and security-oriented use cases. Many AI risk management offerings rely on the mass computing scale achievable in the cloud, where large quantities of unstructured data can be analyzed and processed rapidly.

Risk management analytics that use cloud-based AI (artificial intelligence) can help organizations evaluate the following says Rowan Relton:

uncertain conditions or situations;

the likelihood of a condition or situation occurring based on context; and

the effects the occurrence may have, i.e., the possible outcomes.

Risk management tools that use AI (artificial intelligence) can often be integrated into security automation workflows. Additionally, they can also help security leaders make decisions during incidents, business continuity planning, fraud investigations and more.

Applications of AI in risk management

There are many use cases where AI can benefit risk management and mitigation processes and practices says Rowan Relton. The five most common use cases today include the following:

1. Threat intelligence analysis

Threat intelligence data provides perspective on things such as attacker sources, indicators of compromise, behavioral trends related to cloud account use and attacks against various types of cloud services. Threat intelligence feeds can be aggregated, analyzed at scale using machine learning engines in the cloud and processed for likelihood and predictability models.

With the escalation of account hijacking and ransomware infections, more rapid analysis of data and predictive intelligence could prove invaluable to security teams.

2. Security event management

Log data and other events are being produced in enormous quantities. Security teams need to quickly recognize specific indicators, see patterns of events as they occur and spot events happening in cloud environments. Machine learning and AI can augment massive event data processing technology to build more intelligence detection and alerting tactics. Microsoft’s Azure Sentinel service is an example of a cloud-based, machine learning and AI-focused SIEM.

3. Fraud detection

For financial firms and insurers, fraud detection requires an enormous number of inputs and data types and many intensive types of processing says Rowan Relton. Cloud AI and machine learning engines could help with text mining, database searches, social network analysis and anomaly detection that are coupled with predictive models at scale. This could be extended to things such as fraudulent use of cloud services, for example, an Office 365-based phishing attack from a hijacked account.