The digital world is constantly changing, and with it come new threats to remote work, customer data, and employee privacy. Cyber breaches, confidentiality and privacy issues, third-party risks, and security incidents are among the top IT risks that organizations must be able to manage and assess. To protect against these risks, organizations should carry out a technological risk assessment at least once a year or more frequently as their technological landscape changes or new systems are introduced. Phishing is a type of social engineering attack that uses misrepresentation to obtain sensitive information.
It involves the creation of a false identity or scenario to trick a person into disclosing information. Malware is any software that has a harmful intent, such as stealing or corrupting data, crashing systems, or secretly recording computer activity. It can infect computers through a “pop-up window” that appears while browsing the Internet or after an employee accidentally downloads infected files. Poorly chosen employee passwords can also increase an organization's exposure to security risks.
In addition to these risks, organizations must also be aware of the risks associated with artificial intelligence (AI). AI can introduce risks such as identity theft, data leaks, and potential security breaches. To mitigate these risks, organizations should ensure that users are informed of what data is being collected and obtain their consent; provide proper training and implementation of data sets; test the AI system to ensure it achieves its objectives without allowing unintended effects; implement human oversight of the AI system; and document how and why their AI systems work the way they do. Organizations must be aware of the ever-changing IT risks in order to protect customer, employee, and third-party data.
To do this, they should carry out regular technological risk assessments and be aware of the risks associated with AI. By informing users of what data is being collected, providing proper training and implementation of data sets, testing the AI system, implementing human oversight of the AI system, and documenting how and why their AI systems work, organizations can mitigate these risks and protect their data.
