Man with robotic hand pointing at technology dashboard showing AI and information risk assessments

5 Ways to Implement AI During Information Risk Assessments

Unless you’ve been living under a rock for the past few years, you’ll know that artificial intelligence (AI) is now one of the hottest trending topics in tech and business circles. Every day seems to bring new developments and applications of this evolving technology, which has led to the expectation that AI will revolutionize just about every business sector over the next decade.

However, for all the advantages that new AI models have brought in terms of automation and optimization, they’ve also increased the risk of cyberattacks. For instance, there has been plenty of talk about how ChatGPT is being used by cybercriminals to write malicious code and generate phishing messages at an unprecedented rate. Unless companies give priority to combating these attacks through regular information risk assessments, businesses of all sizes could soon find themselves under an avalanche of system-wide attacks and data breaches.

Fortunately, AI can also be used to defend against these attacks through an AI-based information risk assessment. AI is allowing companies to mitigate potential security risks and even predict future attacks with greater speed and accuracy than they could ever achieve through manual processes. And there are a number of use cases for AI in risk assessment that show how companies can do this.

Automatic data classification

One thing that needs to be clearly understood about the use of AI in data security is that it isn’t intended to completely replace human data analysts. Rather, AI is intended to augment and expand the capabilities of humans through a process known as hybrid intelligence (HI), allowing analysts to do more in less time and with greater ease.

Nowhere is this more apparent than with the task of data classification, during which companies carefully examine all of their data to determine its level of sensitivity and required security. Performing this task manually can be highly time intensive, so much so that it can easily make up half of the time of a company-wide risk assessment. By leaving this task to an AI model, data analysts can focus their efforts entirely on securing data that’s been flagged as sensitive, thus dramatically decreasing the time it takes to complete a risk assessment.

Compromised account detection

Phishing attacks still account for nine out of every ten security breaches. And with cybercriminals now employing AI to create highly convincing-looking phishing messages, even the most careful employees can fall prey to these attacks. Once a bad actor has gained access to a company’s network, detecting that a breach has occurred can be very difficult, resulting in lost time, stolen data, and even system-wide malware infections that can take months to recover from.

Fortunately, AI models can detect potentially compromised user accounts. This is achieved by training the AI model to recognize behavioral patterns from each user. If at any time those patterns begin to drastically diverge from normal, then the AI system can be instructed to lock out that user and flag the account for investigation by the IT security team. All of this happens in real-time, allowing for an instantaneous response the moment a user account starts acting outside its usual behavior, such as installing outside programs or accessing files it doesn’t typically work with.

Automated penetration testing

Cyberattacks can happen at any time, and no organization can afford to let a security vulnerability remain unpatched. The problem is that there is no such thing as a perfectly secure system; there will always be vulnerabilities that an IT team is unaware of. This is why IT teams perform regular penetration tests – simulated attacks to test a system’s security. But humans can’t work 24/7 and with the level of cyberattacks increasing each year, it’s inevitable that attackers will find and exploit a vulnerability before a manual penetration test can detect it.

By turning this task over to AI, companies can run automated penetration tests at any time. These AI models can work in the background and provide immediate alerts the moment a vulnerability is found. Better still, the AI can classify vulnerabilities based on the threat level, meaning if there’s a vulnerability that could allow for a system-wide infiltration, then that vulnerability will be prioritized above lesser threats.

Over-privileged account detection

Out of all the user accounts on an organization’s network, privileged accounts require the most careful security. Privileged accounts are typically admin accounts with access to security-relevant functions or underlying platform features that non-privileged users can’t access. Unsurprisingly, privileged accounts are regularly targeted since they can provide easy access into an organization’s network. But even with the best security in place, privileged accounts can still be hacked. This usually happens because most people tend to reuse the same password, with only slight variations, across multiple sites.

The usual answer to this security challenge is to implement the Principle of Least Privilege (POLP), which restricts users’ access to only the data necessary to do their jobs. That way, if a privileged account is hacked, the damage can be mitigated. For many companies, implementing POLP has never been more important: it’s been estimated that over 70 percent of employees at a typical company have access to data they don’t need.

AI-driven analytics can determine which users have access to data outside their needs. These over-privileged accounts can then have their data access restricted to reduce the chances of a major breach.

Future attack predictions

AI-powered predictive analytics can be an incredibly powerful tool that allows an organization to estimate the results of a marketing campaign, a customer’s lifetime value, or the impact of a looming recession. But predictive analytics can also be used to predict the likelihood of a future data breach.

For example, data on recently discovered vulnerabilities and recent cyberattack activity can be compiled to give an estimate of the likelihood of an attack, when it might take place, and how bad it could be. Knowing this information can be crucial for an IT security team, allowing them to assess their potential vulnerabilities and make plans for how they can prevent or mitigate an attack before it strikes.

Final thoughts

AI is getting more powerful with each passing year. While advances in AI have undoubtedly made it easier for cybercriminals to launch larger and more sophisticated attacks, AI has also made it easier for IT teams to stop or mitigate these attacks. In time, AI will likely become an embedded part of every organization’s security framework, laying a strong foundation for the future of data security.