In just a few short years, “artificial intelligence” (AI) went from being a buzzword to a fact of life. Today, organizations of all sizes and in all industries are leveraging AI-based solutions, and adoption rates show no sign of slowing. But it’s not enough to understand how to leverage AI to improve productivity—it’s also important to understand the dangers that come along with it. Cybercriminals are already finding ways to use the technology to their own advantage, while lax AI policies are allowing data leakage to occur with worrying regulations. With AI poised to play an ever-increasing role in our future, it’s critical for today’s organizations to understand the factors they will need to consider if they want to keep themselves—and their data—safe.
LLM and data compromise threats
As organizations incorporate AI-based solutions across a wide range of business areas—including customer service, analytics, and other functions enhancing or replacing traditional roles—the risk of data compromise will continue to increase. Most “early adopter” organizations leveraging AI capabilities understand that it is important to vet public LLMs to ensure that any data they ingest is not shared. However, as AI adoption continues to increase, not all organizations will have the knowledge or experienced needed to vet providers effectively. This can cause serious issues with potentially significant negative repercussions.
Smaller companies may find LLMs appealing due to the increased efficiencies they enable, but they may not know what to look for when it comes to disclosure agreements. As a result, they may find themselves in bed with unscrupulous providers with poor security standards (or even providers that actively share information from users). Some enterprises may also fall prey to technical or human errors, such as conducting tests with real data or using non-private LLMs. This can result in sensitive information being leaked, which can create major security, privacy, and compliance concerns. It’s also important to remember that AI remains a relatively new technology. It makes mistakes, and it can be manipulated. Attackers are already finding ways to intentionally exploit AI to expose data or prompt it to perform unintended actions, and organizations need to be prepared to deal with those challenges.
The danger of AI in the hands of attackers
Much has been made of AI and its potential dangers in the hands of attackers. It’s true—with the help of AI, launching an attack has never been easier, and it’s likely just a matter of time until we witness a significant AI-driven breach. That said, all is not lost. AI-specific security controls are already beginning to emerge, and as AI becomes more commonplace, newer and more advanced solutions will continue to emerge in the near future.
These controls are focused on several key areas. First, it’s important to ensure AI engines don’t expose sensitive data to unauthorized users. It’s not uncommon for attackers to attempt to manipulate an AI-powered chatbot to reveal client data or other private information, so putting safeguards in place is essential. On a similar note, it’s also important to prevent unauthorized data from being indexed by LLMs. If an employee accidentally uploads sensitive information to a public or untrusted LLM, the resulting data leakage can be just as damaging as if an attacker had stolen it. Organizations also need to be able to prevent AI from performing unauthorized actions while maintaining clear visibility and control over any applications using LLMs, including managing data access permissions and settings.
Some may view these controls as simply an extension of traditional DLP, WAF, CASB, and database firewalls, and it is true that they will—someday—likely be integrated into large, consolidated security suites. However, until that time, organizations need to ensure they have the solutions in place to help them stand up to increasingly dangerous AI-based threats.
Emerging AI regulations
Regulations almost always lag behind innovation, and AI is no exception. While a handful of AI regulations have begun to emerge around the world, most organizations are currently taking matters into their own hands by implementing dedicated AI polices to evaluate and control the AI services they use. Right now, those initiatives are focused primarily on maintaining data privacy and preventing AI from making critical errors. These AI safety standards will continue to evolve and will likely be integrated into existing security frameworks, including those put out by independent advisory bodies. Regulators will almost certainly maintain a strong focus on ethical considerations, creating guidelines that help define acceptable and responsible use cases for AI capabilities.
While the trend toward stronger AI governance is clear, significant questions remain. If an AI-based solution allows a user to commit a crime, where does the liability lie? Does the provider bear some responsibility for the act? AI presents a complex challenge for lawmakers, who will need to strike a balance between its potential benefits and its inherent risks. Give the rapid evolution of the AI landscape, this is easier said than done. Regulators will have their work cut out for them as innovative new AI-based products and services continue to hit the market.
Recognize and overcome AI challenges
AI-based solutions are already having an outsized impact on organizations across a wide range of industries. But as adoption soars, it’s important to ensure that security and business leaders have a thorough understanding of the potential dangers that accompany the technology. Used improperly, LLMs can result in data leakage and other damaging outcomes, while malicious attackers are seeking ways to leverage the technology for their own ends. And although the AI regulatory landscape is beginning to take shape, it’s difficult to predict exactly what form future legislation may take. As businesses continue to use AI tools, recognizing and understanding these challenges is the first step toward overcoming them.