With rapid technological advancements come cyber vulnerabilities and its defence mechanisms. Detecting anomalies in the system by leveraging the capabilities of generative AI is imperative.
Generative AI is fuelling industries for various reasons and rightly so. Security leaders are embracing the advancement for more precise and informed decision-making. For instance, financial institutions have started to integrate this technology into their services for task automation and enhanced customer experience through valuable business insights.
With rapid technological advancements come cyber vulnerabilities and its defence mechanisms. Detecting anomalies in the system by leveraging the capabilities of generative AI is imperative.
What is anomaly detection and why is it essential?
Anomaly detection is the process of identifying irregularities or deviations in datasets ( customer’s transaction history, object image, etc.), further executing informed actions to prevent them. In simple terms, it can be a suspicious activity falling against your defined normal patterns of behaviours.
Enlisted below are a few reasons why anomaly detection is of dire importance:
- Keeps you informed before the event occurs by sending predictive insights
- Safeguards your digital assets and customer’s sensitive information from unprecedented data breaches
- Protects your business from irrevocable loss of reputation
Also Read: The Increasing Role of Generative AI in the Digital Economy
How are various industries utilising Generative AI for anomaly detection?
Several industries such as BFSI, IT and manufacturing are inclining towards generative AI algorithms for detecting anomalies in their systems.
For instance, banks and financial institutions utilise generative AI algorithms to identify anomalies within large datasets, including transaction irregularities, user behaviours indicating fraudulent activities, etc.
After receiving valuable insights into these suspicious behaviours, financial institutions can take immediate action to avoid unauthorised access and transactions. Enlisted below are ways in which generative AI has helped industries detect anomalies in near real-time:
Alerts institutions about fraudulent activities
Utilising the GenAI-driven real-time insights related to malicious activities, companies can immediately send alerts to their customers at a scale. As a result, it enables them to take relevant actions to protect and guard their personal data.
Trains AI models for adversarial learning
- With the help of Generative AI algorithms and logic, data teams working within organisations can train their AI models-a generator and discriminator against each other. For instance, When the generator generates synthetic data(unrealistic data) to imitate real-world transactions in financial institutions, the discriminator identifies the authorised data. As a result, this process can improve fraud detection, further making the systems resilient to attacks and cyber threats.
Conclusion, in a world full of AI innovations and advancements, malicious actors aren’t behind in utilising AI-driven tactics to breach user data. Hence, it becomes essential for organisations to incorporate generative AI capabilities to detect system anomalies in real-time. Furthermore, it will enable them to send live notifications to their customers to protect their sensitive data.
