As the adoption of generative AI (GenAI) soars, so too does the risk of insider threats. This puts even more pressure on businesses to rethink security and confidentiality policies.
In just a few years, artificial intelligence (AI) has radically changed the world of work. 61% of knowledge workers now use GenAI tools — particularly OpenAI’s ChatGPT — in their daily routines. At the same time, business leaders, often partly driven by a fear of missing out, are investing billions in tools powered by GenAI. It’s not just chatbots they’re investing in either, but image synthesizers, voice cloning software and even deepfake video technology for creating virtual avatars.
We’re still some way off from GenAI becoming indistinguishable from humans. Even if — or perhaps when — that actually happens, then the ethical and cyber risks that come with it will continue to grow. After all, when it becomes impossible to tell whether or not someone or something is real, the risk of people being unwittingly manipulated by machines surges.
GenAI and the risk of data leaks
Much of the conversation about security in the era of GenAI concerns its implications in social engineering and other external threats. But infosec professionals must not overlook how the technology can greatly expand insider threat attack surface, too.
Given the rush to adopt GenAI tools, many companies have already found themselves getting in trouble. Just last year, Samsung reportedly banned the use of GenAI tools in the workplace after employees were suspected of sharing sensitive data in conversations with OpenAI’s ChatGPT.
By default, OpenAI records and archives all conversations, potentially for use in training future generations of the large language model (LLM). Because of this, sensitive information, such as corporate secrets, could potentially resurface later on in response to a user prompt. Back in December, researchers were testing ChatGPT’s susceptibility to leaking data when they uncovered a simple technique to extract the LLM’s training data, thereby proving the concept. OpenAI might have patched this vulnerability since, but it’s unlikely it’ll be the last.
With the unsanctioned use of GenAI in business growing fast, IT must step in to seek the right balance between innovation and cyber risk. Security teams might already be familiar with the term Shadow IT, but the new threat on the block is Shadow AI or the use of AI outside the organization’s governance. To prevent that from happening, IT teams need to revisit their policies and take every possible step to reinforce the responsible use of these tools.
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Proprietary AI systems carry unique risks
An obvious way to address these threats might be to build a proprietary AI solution tailored to the specific business use case. Businesses may build a model from scratch or, more likely, start with an open-source foundation model. Neither option is without risk. However, while the risks that come with open-source models tend to be higher, those concerning proprietary AI systems are a little more nuanced —and every bit as serious.
As AI-powered functions gain traction in business software applications, they also become a more appetizing target for malicious actors — including internal ones. Data poisoning, where attackers tamper with the data used to train AI models, is one such example. The insider threat is real, too, especially if the data in question is widely accessible throughout the organization, as is often the case with customer service chats, product descriptions or brand guidelines. If you’re using such data to train a proprietary AI model, then you need to make sure its integrity hasn’t been compromised, either intentionally or unintentionally.
Malicious insiders with access to proprietary AI models may also attempt to reverse engineer them. For instance, someone with inside knowledge might be able to bypass audit trails since proprietary systems often have custom logging and monitoring solutions that might not be as secure as their mainstream counterparts.
Secure your AI software supply chains
The exploitation of model vulnerabilities presents a serious risk. Whereas open-source models may be patched quickly through community involvement, the same can’t be said of the hidden flaws that a proprietary model might have. To mitigate these risks, it’s vital that IT leaders secure their AI software supply chains. Transparency and oversight are the only ways to ensure that innovation in AI doesn’t add unacceptable risk to your business.
