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:
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, says Rowan Relton.
