Secure Your AI Pipelines with DSPM

AI is omnipresent today. You may have seen stories on social media about AI models deleting entire code bases, or how malicious actors could exploit AI models in an environment to their advantage. Maybe your company began to use an enterprise LLM, and there are data leakage concerns. In a survey of 200 U.S. IT directors, Komprise found that 79% of IT leaders reported having a negative outcome from employee use of generative AI. Being involved in cybersecurity means constantly worrying about how technologies may be taken advantage of, and AI and their pipelines are no exception.

Data Security Posture Management (DSPM) tools seek to solve these issues by not only securing the data that AI may interact with, but also providing visibility into AI models and the users that interact with them. DSPM is a tool that gives organizations a clear view of their data: what they have and where it lives. Through this discovery, data can be categorized, classified, and sorted by sensitivity.

AI has an inherently close relationship with data—it both trains from it and interacts with it. Safeguarding data prior to model training and ensuring controlled interactions substantially mitigates AI risk within enterprise settings, an area where DSPM plays a critical role.

Some common issues for organizations regarding AI models, data pipelines and associated users include:

• AI models training on sensitive data and customer information that should not be available to the model
• LLMs outputting sensitive information to the wrong or unintended users
• Insider threats prompting LLMs to override any security controls
• Forgotten/Shadow AI models burning through cloud compute costs
• Users uploading customer information to public LLMs such as ChatGPT or Gemini
• Users accessing public or private models that they shouldn’t

How DSPM can help:

DSPM has evolved from a basic data discovery tool to a dynamic solution that helps address an organization’s data attack surface, including the issues outlined above. The following are key ways DSPM can help protect AI.

Training Data Visibility:

DPSM can mark data as sensitive. This allows AI engineers to make informed decisions on what data they can use to train their models and ensures that integral data like customer information is never used. DSPM can integrate with LLMs such as CoPilot to directly mark data as safe or unsafe for CoPilot to train from, reducing any associated risks.

Some DSPM tools also allow for a more user-centric approach to data. As it relates to AI, this capability allows cybersecurity teams to see who has access to AI training data and who has made any modifications. This greatly reduces the risk of insider threat through AI backdoors with attacks such as data poisoning and ensures that data is in the right hands before it’s used for training.

AI Security Posture Management:

As more organizations deploy AI models within their environments, DSPM has expanded its visibility capabilities to include these models, strengthening overall security posture. DSPM can find all AI systems in an organization, even “Shadow AI” that operates out of view but still relies on company data and resources.

Furthermore, as DSPM has begun to include more user information in its findings, many tools can also see who has access to AI models and how they may be using them, and this provides insight into both ROI and potential insider threats.

Through AI discovery, DSPM can make note of what tools and databases AI models are accessing. By using this to apply least privilege and restrict excessive autonomy, organizations can prevent AI models from abusing permissions or causing permanent harm, like deleting codebases or changing user roles.

AI Runtime and LLM Visibility:

DSPM has extended its reach to also govern LLMs that users may be interacting with. This allows visibility into conversations that users are having with organization LLMs, with some DPSMs even blocking messages that are deemed as risky from either the user or the AI. All conversations can be logged, and user baselines can be utilized to better detect anomalous conversations and decrease the potential for insider threats and prompt injection.

To restrict users from using public LLMs, DSPM vendors are creating browser extensions that can detect if a website is an AI chat. The extension then blocks the user from interacting with the website, greatly reducing the probability of data leakage to AI companies.

AI continues to integrate into nearly every aspect of corporate life, and keeping pace with its rapid growth can feel overwhelming. By leveraging AI’s dependency on data, DSPM helps organizations stay proactive by providing visibility into how AI, users, and data interact, along with actionable insights to address related risks. Securing AI and their pipelines with DSPM allows any organization to evolve and adapt to cybersecurity’s rapidly changing landscape.

Works Cited

Komprise, "Komprise Survey Finds that Shadow AI is a Major Concern across Enterprise IT"