AI, Cybersecurity Tools & You
“Artificial intelligence” is a vague term that refers to an entire field of research in machine learning, under which generative AI like ChatGPT is only a part. As the field has exploded over the recent years, scammers and cybersecurity teams alike have raced to incorporate the still-rapidly developing technology into their schemes and workflows.
Understanding how AI intersects with cybersecurity and scams is crucial for protecting yourself and your organization against the latest attacks. While AI has some benefits for security teams, it can also benefit those willing to commit fraud.
For further reading and extra tips on how to protect yourself from fraud, check out Security Federal Savings Bank’s Learning Center.
1. How is AI used in fraud?
Generative AI has made it easier than ever before for anyone to start scamming others. Large language models (LLMs) as well as video and image generation tools mean that fraudsters barely need to lift a finger to become a coherent threat in any language they choose.
With LLMs, social engineering schemes such as phishing are as simple as pushing a few buttons – and the scammer doesn’t even need to know English to try to convince you to hand over your account information or credit card details. They can use AI to create believable fake websites, social media accounts, emails or coupons – or to scrape data about their victim off their social media profiles and internet presence, allowing the scammer to focus their attacks with greater precision.
Deepfake and voice cloning technology has also been leveraged by scammers to improve their targeted attacks, and as more companies invest in generative AI, these tools are only becoming more and more accessible. Deepfake technology allows anyone to create a video with anyone’s face, and AI voice cloning lets them talk in anyone’s voice – obvious reasons why this technology can be dangerous.
2. How is AI used in cybersecurity?
Whether you’re a private user or a small business owner, you’ve likely purchased a number of third-party applications and security programs. Some of these may also be newly incorporating generative AI, a relatively young technology without a lot of standard regulations yet.
Of course, generative AI cannot replace a real human cybersecurity team, because real humans will be needed to combat more complicated cases, but it can help. Generative AI may be used to help automate routine tasks, such as sorting or responding to support tickets, which helps security teams react quickly to the more urgent or complex cases. Beyond those situations, however, you start opening yourself up to the weaknesses of generative AI and its security risks.
Thankfully, generative AI tools for cybersecurity are not the be-all and end-all. Other types of machine learning in cybersecurity can help spot threats and analyze large amounts of data much faster than humans. A powerful algorithm that’s focused on the organization’s needs can:
- Identify potential vulnerabilities that haven’t yet been noticed by the organization
- Continuously monitor and track human users’ habits and take action when something out of the norm happens
- Instantly lock down systems or profiles if it detects a breach of security
Always remember: An AI cybersecurity tool cannot be held accountable for itself. It is critical to ensure that a human security team is always aware of what the AI tool is doing – because if it makes a mistake, the consequences fall on the humans.
3. What are the drawbacks to AI in cybersecurity?
AI does have security vulnerabilities. Some are more well known, such as an LLM’s tendency to “hallucinate” and just make up information, but some vulnerabilities are often overlooked.
Many AI tools keep their complex algorithms tightly locked “under the hood,” so to speak, which means users can’t tell how an algorithm arrived at a certain conclusion. That lack of transparency, combined with the ingrained biases that AI scrapes up during its algorithmic learning, inevitably leads to difficulties managing the AI tool – and, at times, requires cleaning up after its messes.
Additionally, while AI can’t be hacked in the traditional sense, it has other unique vulnerabilities and AI cybersecurity threats. Generative AI is susceptible to “poisoning,” where a hacker infiltrates the model and trains it in a way that’s better for them and worse for you. Plus, these LLMs can become the targets of “adversarial attacks,” where a user inputs carefully crafted prompts to obtain private data or get around an established security system.
In Conclusion
AI is a powerful tool that has the potential to improve cybersecurity both in your personal life and workplace, but the risks of AI in cybersecurity also cannot be overlooked. Plus, scammers have access to the same AI tools as everyone else and can use those tools to aid their schemes. In the end, humans still play a critical role in keeping your information safe and secure.
For more information or additional help or resources regarding your digital security, don’t hesitate to reach out to the Security Federal Savings Bank team.