By now you’ve probably heard the example of the airplane when it comes to artificial intelligence (AI) and cybersecurity. If not, cybersecurity for AI has been compared to an airplane that is built or assembled while in flight.
This is an apt image in this case, as many of us try to follow up with something that has already taken. A similar analogy can be made with data, which can be compared to airplane fuel. No one really knows what is in the tank at any time and this can lead to great risks.
Data protection traditionally focuses on data at rest and data in transit leaving gaps when it comes to AI platforms. Data Security Status Management (Data Security Posture Management – DSPM) helps organizations identify and secure the data in use—the data that powers AI. Let’s look at the ways in which DSPM addresses the main AI data challenges.
Key conclusions
- AI introduces new risks of data exposure during training, prompting and output
- Traditional data security lacks visibility into AI workflows
- DSPM helps identify, classify and track sensitive data used by AI
- Security teams can reduce risk without slowing innovation
How artificial intelligence is changing risk in data security
Data is constantly moving between applications, models, and application programming interfaces (APIs). Sensitive data can be inadvertently fed into AI systems, and the results can expose proprietary or regulatory or regulatory information. Visibility gaps extend to (model) training and inference.
A common example of this is when an employee pastes sensitive data (client files, source code, or contract details, etc.) into an AI tool like ChatGPT to get help with a task, such as creating an executive summary or an executive summary, or a developer uses an AI assistant to write code and paste proprietary or confidential code. Now, the data is processed outside the organization’s environment and the risk is already planted.
What security issues does using DSPM for AI solve?
To protect all data, data security for artificial intelligence must address critical security challenges that traditional tools often overlook. DSPM answers critical questions for security teams:
- Where is sensitive data used in AI pipelines?
- Who interacts with the data?
- What models or tools have access to the data?
- Are these exposed externally?
DSPM can detect misconfigurations in complex cloud and software-as-a-service (SaaS) environments, reducing unanticipated risk. By adding context and priority, it allows teams to focus on the most critical threats rather than getting lost in the noise of alerts.
Why is traditional data protection struggling with AI platforms?
Limited visibility into AI data streams
Traditional tools focus on static environments rather than dynamic AI pipelines, such as application programming interfaces (APIs), SaaS tools, integrations, and vector databases.
Inability to track data usage in real time
They cannot track how data is used in prompts, training, or results, which can come and go in an instant.
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 problems after exposure, not before. A DSPM solution detects dangerous exposure before it is used in artificial intelligence.
How DSPM helps organizations secure AI data
DSPM solutions help organizations secure AI data by providing continuous visibility into where sensitive information is located and how it flows through AI systems, including prompts, patterns and results.
Discovery -Find shadow data (shadow data) used in artificial intelligence
Classification / classification -Mark sensitive data even in unstructured formats used by artificial intelligence
Surveillance -Recording how data is used in prompts, application programming interfaces (APIs), and model interactions
Priority – Reduce alert fatigue by concentrating high-risk exposures associated with AI workflows.
Before DSPM, organizations could not know what data was going in or out of AI systems. Now, a DSPM solution allows teams to monitor, control and manage AI data streams without hindering their use.
Now, when an employee uploads a document with customer data to an AI tool, DSPM will identify sensitive data, flag external exposure, warn or block the action based on security policies, and then provide relevant details.