Using Generative AI for Better Data Security
Generative AI can play a role in enhancing data security by providing innovative solutions to address various challenges. This article, https://supportyourapp.com/call-center-outsourcing discusses this interesting concept in greater detail.
Why Consider Generative AI At All?
According to Forbes, generative AI poses a real threat to data security as bad actors are using programs like Chat GPT in increasingly inventive ways. Cybercriminals are using AI-powered malware to evade normal detection measures. Furthermore, they’re utilizing the technology to overcome more sophisticated systems like biometric checks and creating deepfakes to con key employees.
AI’s ability to converse like real humans makes phishing easier and more effective, while its adaptability makes it ideal for launching advanced persistent threats. So, basically, even if you don’t use AI, you have to beef up your security to defend against it.
How Can You Use Generative AI for Better Data Security?
Should you allow AI to have free reign when it comes to sensitive customer data? The jury’s out on this one, but most experts caution against doing so. AI learns as it consumes data, so while it’s unlikely to reveal actual consumer data, it can pick up patterns like marketing strategies.
If you use an open AI learning model like Chat GPT, it might pass on these strategies when disseminating information to other users. Therefore, it’s probably better to keep some level of separation between AI and sensitive information or to develop an in-house model.
Improve security using AI:
However, there is still much you can accomplish to improve security using AI in other areas, such as:
- Training employees to recognize phishing attempts by creating realistic models and random tests.
- Detecting anomalies in the way your employees access or use data.
- Creating secure passwords through random password generation.
- Adding additional security measures such as behavioral biometrics which can analyze traits that are difficult to replicate, like typing speed.
- Creating fake data to disguise the real data. While this won’t prevent hacks, it can make the cost of accessing the real information too onerous to make it worthwhile.
- Redacting the contents of sensitive documents or communications so that if bad actors access it they can’t gather anything useful. You could, for example, program the algorithm to obscure names, account numbers, and other identifying factors.
- Identifying system weaknesses and potential entry points. Bad actors can create malware that can search for weaknesses, so it makes sense to do the same. Using generative AI is particularly useful here because it can think outside the box and adapt its strategy.
- Creating better access authentication methods. AI makes it possible to check several authentication factors at once. Companies can, therefore, incorporate biometric authentication with random password generation and even check typing speeds for extra security.
- Coming up with new security simulations. AI can analyze many data points varying from cybersecurity risks to physical incursions. They can come up with realistic scenarios that allow the company’s cyber and physical security teams to better prepare.
- When a breach occurs, fast action is integral in minimizing the damage. AI can help to detect potential issues and automatically implement an incident response plan.
- AI can also test new software or CRM systems to identify potential bugs that bad actors could exploit at a later stage.
Conclusion
Generative AI is here to stay, so we can’t pretend it doesn’t exist. Bad actors are making good use of this tool, and so should we. By using AI to scan for risks and create realistic scenarios to train employees, we can improve data security.
Should we rely on AI on its own? Most certainly not, but that doesn’t mean that we need to cower from it either.
