After a year full of excitement and uncertainty, with countless opinions floating around about the future of AI, AI providers, businesses, and policymakers are now coming together on one thing: AI security.
White House recently Following in the footsteps of the European Union, we have introduced a new set of standards for the secure development and deployment of AI models. But while regulators triangulate policy and AI companies strive to comply, the real responsibility for operating safely lies with companies.
As companies seek to securely integrate generative AI into their products and services (and some Not so good results), it is essential to understand why generative AI challenges traditional thinking about cybersecurity, and what prompted the need for AI regulation in the first place.
It’s so easy that even kids can do it
Unlike any technology we’ve seen so far, generative AI attacks are limited only by the English language. The attack could be carried out by literally anyone, from a novice hacker to her 10-year-old to grandma and grandpa.
For other types of software, bypassing cybersecurity protection measures requires a malicious attacker to have at least moderate knowledge or experience with the coding language or IT infrastructure. All generative AI needs is creativity and malicious intent.
In a sense, creativity is the new hacker currency. It is used to create and carry out attacks that cannot be detected and prevented by traditional cybersecurity measures.and 72 percent of white hat hackers We believe that they are more creative than AI, so we assume that a malicious attacker with a similar skillset will only need a little more creative ability to cause serious problems at scale. It is safe to do so.
From persistent nagging to creative wordplay, hackers can trick AI models into performing unintended functions and revealing information that should otherwise be protected. These prompts don’t have to be complicated, and malicious attackers are always looking for new ways to obtain generative AI models and divulge secrets.
The threat landscape for companies innovating with AI is becoming more complex. So what should we do about it?
Industry-wide cooperation is essential
Just as there are many different ways to express a message in English, the same is true for LLM hacks. There are countless ways that AI models can be used to generate harmful or racist content, expose credit card information, or spread misinformation. The only way to effectively protect your AI apps from this plethora of attack vectors is through data. We have a lot.
Protecting against AI threats requires extensive knowledge of what those threats are. AI security requires an unprecedented compilation of threat data, as possible attack vectors grow every day. There is no single source or company that can collect the data needed to adequately protect LLM alone. AI security requires a collaborative effort across the industry.
We started seeing this development at DEFCON31. At DEFCON31, white hat hackers came together to stress test popular generative AI models, discover vulnerabilities, and share the results. Most recently, the Biden administration required all LLMs to undergo safety testing and share their results with the U.S. government. In addition to government-led initiatives, Open source dataset is emerging and will also play a key role in pooling AI security data. Community-based initiatives like this are essential, and I hope that collaboration will be further strengthened in the future.
The second act of cybersecurity
Cybersecurity teams have never had it easy. The frequency and complexity of cyber-attacks is steadily increasing every year, and the shortage of cyber talent is making the situation even worse. The advent of generative AI is the latest of his 100-pound weights on the backs of cyber teams.
As product teams integrate hundreds of Generative AI applications across their organizations, traditionally employed security solutions such as firewalls cannot address the inherent risks of AI. Generative AI is more accessible than ever. Now is the time for cybersecurity leaders to understand the nuances of cybersecurity second acts to protect their organizations from AI cyber risks. Organizations must carefully consider how they can safely and securely deploy generated AI applications into production environments. Educating and supporting this group is essential to the success of your company’s AI initiatives.
Only by understanding and internalizing the fundamental risks can organizations begin building their own security processes and standards and safely deploy AI into the real world.
David Haber is the co-founder and CEO of . Laquera.