By now, you've probably heard the airplane analogy when it comes to AI and cybersecurity. If not, cybersecurity for AI has been likened to a plane being assembled while airborne. This is a fitting visual as many of us are trying to keep up with something that is already taken off. With this same analogy, you can start to think of data as the plane fuel. No one really knows what's in the tank at all times and that can lead to big risks. Traditional data protection focuses on data at rest and in transit, leaving gaps when it comes to AI platforms. Data Security Posture Management (DSPM) helps organizations discover and secure the data in use – the data fueling AI. Let’s explore how DSPM addresses key AI data challenges.
Key Takeaways
- AI introduces new data exposure risks across training, prompts, and outputs
- Traditional data security lacks visibility into AI workflows
- DSPM helps identify, classify, and monitor sensitive data used by AI
- Security teams can reduce risk without slowing innovation
How AI Changes Data Security Risk
Data is constantly moving between apps, models, and APIs. Sensitive data may be unintentionally fed into AI systems and outputs can expose regulated or proprietary information. The visibility gaps expand across both training and inference. A common example of this is an employee pasting sensitive data (customer records, source code, or contract details, etc.) into an AI tool such as ChatGPT to assist in a task such as summarizing or a developer uses an AI coding assistant and pastes proprietary code. Now the data is processed outside the organization’s environment and the risk has been planted.
What Security Problems Does DSPM for AI Solve?
To protect all data, data security for AI must address critical security challenges that traditional tools often miss. DSPM answers critical questions for security teams:
- Where is sensitive data being used in AI pipelines?
- Who is interacting with the data?
- Which models or tools have access to the data?
- Is it being exposed externally?
DSPM can detect misconfigurations across complex cloud and SaaS environments, reducing unintended risk. By adding context and prioritization, it enables teams to focus on the most critical threats rather than getting lost in noise.
Why Traditional Data Protection Struggles with AI Platforms?
Limited Visibility into AI Data Flows
Traditional tools focus on static environments, not dynamic AI pipelines such as APIs, SaaS tools, embeddings, vector databases.
Inability to Track Data Usage in Real Time
They can’t monitor how data is being used in prompts, training, or outputs which can happen and disappear.
Gaps in data classification
Sensitive data is often unstructured or embedded in AI workflows making it difficult to classify.
Reactive vs proactive security
Legacy tools detect issues after exposure, not before. DSPM identifies risky exposure before it's used in AI.
How DSPM Helps Organizations Secure AI Data
DSPM solutions help organizations secure AI data by providing continuous visibility into where sensitive information lives and how it flows across AI systems, including prompts, models, and outputs.
Discovery - Find shadow data used in AI
Classification - Label sensitive data even in unstructured formats used by AI
Monitoring - Track how data is used across prompts, APIs and model interactions
Prioritization - Reduces alert fatigue by focusing on high-risk exposure tied to AI workflows.
Before DSPM, organizations could not know what data was entering AI systems or leaving them. Now, DSPM enables teams to see, control, and govern AI data flows without blocking usage.
Now when an employee uploads a document with customer data into an AI tool, DSPM will detect the sensitive data, flag external exposure, alert or block based on policies, and provide details.
