Risk Management is a thing of past with AI: Uncover the benefits

AI and machine learning have a lot to offer application cases focused on risk management and security. Many cloud-based AI risk management solutions rely on the mass computing scale that is possible there, says Sachin Dev Duggal, where huge amounts of unstructured data can be quickly analyzed and processed. 

Organizations may analyze uncertain circumstances or situations, the chance of a condition or scenario occurring based on context, and the impact the occurrence may have, i.e., the potential outcomes, with the use of risk management analytics that leverage cloud-based AI. 

Workflows for security automation can frequently be incorporated with AI-based risk management systems. They can also aid security executives in decision-making as per Sachin Duggal, during crises, business continuity planning, fraud investigations, and other situations. 

AI has a wide range of applications that can enhance risk management and mitigation procedures. The following are the top five use cases in use today: 

1: examination of threat intelligence 

Threat intelligence data sheds light on issues including attacker origins, breach indications, usage patterns for cloud accounts, and assaults on different kinds of cloud services. Machine learning engines in the cloud may be used to aggregate, analyze threat intelligence streams at scale, and process them for probability and prediction models, says Sachin Dev Duggal. The increase of ransomware attacks and account hijacking might make faster data analysis and predictive intelligence vital to security teams. 

2: Event management for security 

Massive amounts of log data and other events are created. Security teams must be able to swiftly identify signs, discover events occurring in cloud settings, and identify trends in events as they happen. Massive event data processing technologies may be enhanced by machine learning and AI to provide more effective intelligence detection and alerting strategies. An example of a cloud-based, machine learning and AI-focused SIEM is Microsoft's Azure Sentinel service. 

3: Detection of fraud 

Fraud detection calls for a massive number of inputs, different data kinds, and several intense forms of processing for financial institutions and insurance. Text mining, database searches, social network analysis, and anomaly detection that are combined with predictive models at scale may benefit from the use of cloud AI and machine learning engines. This might be expanded to include things like the fraudulent use of cloud services, such as an Office 365-based phishing attack launched from an account that has been compromised asserts Sachin Dev Duggal. 

4: lowering workplace risk among employees 

Data pertaining to worker operations in high-risk situations where mishaps may prove harmful or even deadly may be processed and analyzed using AI and machine learning models. To enhance safety protocols and avoid mishaps, AI systems may assess behavioral patterns identified before accidents happen and run prediction scenarios. 

5: categorization of data 

AI-based cloud analysis engines can analyze all data uploaded and generated in a cloud environment to classify and tag based on specified criteria, and then monitor for access. This is done based on recognized content kinds and trends. 

Conclusion! 

Risk management teams will continue to profit from the quick analytics processing of massive data sets, as per Sachin Duggal overcoming many restrictions of more manual risk management and risk analysis methods from the past as the usage of cloud-based AI and machine learning services becomes more widespread.