We are all still understanding the exciting possibilities that Generative AI (GenAI) AI is a technology that can create realistic and innovative content, such as images, text, audio, and video. Its use cases span the entire enterprise and can enhance creativity, improve productivity, and generally help people and businesses work more efficiently. We are at a time when no other technology will transform the way we work more radically than AI.
However, generative AI also poses significant cybersecurity and data risks. From seemingly innocuous messages entered by users that can contain sensitive information (which AI can then collect and store) to the creation of large-scale malware campaigns, generative AI has almost single-handedly multiplied the ways modern businesses can lose sensitive information.
Most LLM companies are just beginning to consider data security as part of their strategy and customer needs. Companies must adapt their security strategy to address this, as GenAI security risks are proving to be multifaceted threats that arise from how users inside and outside organizations interact with the tools.
What we know so far
GenAI systems can collect, store, and process large amounts of data from a variety of sources, including user prompts. This ties into the top five risks organizations face today:
- Data leaks: If employees enter sensitive data into GenAI prompts, such as unpublished financial statements or intellectual property, companies expose themselves to third-party risks similar to storing data on a file-sharing platform. Tools such as ChatGPT Or Co-pilot may also disclose such proprietary data in responding to requests from users outside the organization.
- Malware attacks: GenAI can generate new types of complex malware that can evade conventional detection methods. This means that organizations are likely to face a wave of new zero-day attacks. Without defenses specifically designed to prevent them from succeeding, IT teams will struggle to keep up with malicious actors. Security products must use the same technologies at scale to keep up with and stay ahead of these sophisticated attack methods.
- Phishing attacks: This technology excels at creating convincing fake content that mimics real content but contains false or misleading information. Attackers can use this fake content to trick users into revealing sensitive information or taking actions that compromise the company’s security. Malicious actors can create new phishing campaigns—with believable stories, images, and videos—in minutes, and companies will likely see a higher volume of phishing attempts because of it. Deep fakes are produced to impersonate voices for targeted social engineering hacks and have proven to be very effective.
- Bias: LLMs can become biased in their responses and potentially return misleading or erroneous information from models that were trained with biased information.
- Inaccuracies: We have also found that LLMs can accidentally provide the wrong answer when analyzing a question due to a lack of human understanding and the full context of a situation.
Prioritize data security
Mitigating generative AI security risks essentially revolves around three pillars: employee awareness, security frameworks, and technology.
Training employees to securely handle sensitive information is nothing new. But introducing generative AI tools into the workforce requires addressing the inevitable new data security threats that come with it. First, companies need to ensure employees understand what information they can and cannot share with AI-based technologies. Similarly, we need to raise awareness about the rise in malware and phishing campaigns that can result from generative AI.
The way businesses operate has become more complex than ever, making securing data, no matter where it resides, a business imperative. Data continues to migrate from traditional on-premises locations to cloud environments, and users are accessing data from anywhere and trying to keep pace with varying regulatory requirements.
Traditional data loss prevention (DLP) capabilities have been around forever and are very effective for their intended use cases. But as data moves to the cloud, DLP capabilities must also evolve while expanding their capabilities and coverage. Enterprises are now adopting cloud-native DLP, prioritizing a unified application to extend data security across important channels. This approach streamlines out-of-the-box compliance and promises enterprises industry-leading cybersecurity no matter where data resides.
Using data security posture management (DSPM) tools also provides additional protection. AI-powered DSPM products improve data security and protection by quickly and accurately identifying data risks, facilitating decision-making by examining the content and context of the data, and even remediating risks before attackers can exploit them. This prioritizes essential transparency into data storage, access, and usage so organizations can assess the security of their data, identify vulnerabilities, and take steps to mitigate risks as effectively as possible.
Platforms that combine innovations like DSPM and DLP into a unified product that prioritizes data security everywhere are ideal: they connect security capabilities wherever data exists.
Successfully implementing generative AI can significantly improve an organization’s performance and productivity. However, it is essential that businesses fully understand the cybersecurity threats that these new technologies can introduce into the workplace. With this understanding, security professionals can take steps to reduce their risks with minimal business impact.
Jaimen Hoopes, Vice President, Product Management, Forcepoint
