As artificial intelligence (AI) technology matures, many organizations are building their own AI infrastructure to make better informed, data-driven decisions, automate routine tasks, and improve customer experiences.
However, AI infrastructure relies on massive datasets—often containing sensitive information—and complex workflows, which makes it naturally vulnerable to data security threats. This article will explore how data security posture management (DSPM) tools can help organizations protect their AI infrastructure.
Data Security Threats to AI Infrastructure
Before we discuss DSPM’s capabilities, we must first understand why AI infrastructure is vulnerable and what threats it faces. Here are a few examples.
Unauthorized Data Access and Exposure
AI infrastructure is only as good as the data it ingests. Organizations must feed AI infrastructure comprehensive, diverse data sets to ensure the model can identify patterns and make accurate predictions and decisions. However, security teams risk unauthorized data access and exposure if this data is not appropriately stored or protected.
We’ve already seen the risks of improperly stored AI infrastructure materialize. In September 2023, Microsoft AI researchers overlooked a misconfigured SAS token in a cloud storage environment and accidentally exposed 38TB of sensitive information.
Compliance Violations
Data used to feed AI infrastructure is subject to data protection regulations like any other. Organizations building AI infrastructure must ensure that their data complies with all relevant laws, such as GDPR and HIPAA.
Again, we’ve already seen organizations suffer huge fines for failing to ensure AI data complies with data protection regulations. Most famously, in September 2024, the Dutch data protection authority fined Clearview AI €30.5 million for populating its enormous image database partly by scraping the internet for people’s selfies – without asking for their consent.
Data Poisoning and Manipulation
AI infrastructure training data is also subject to external threats. Malicious actors can intentionally manipulate training data to influence AI model outcomes, leading to unreliable results or exploitable models.
Research published in Dark Reading earlier this year revealed the extent of this risk. Researchers discovered around 100 machine learning (ML) models on the Hugging Face AI platform that, when downloaded, would enable attackers to inject malicious code onto user machines.
Overexposure of Model Data in Development Environments
AI development environments often lack the access, encryption, and monitoring controls of their production counterparts. While this can streamline development processes, it also makes these environments vulnerable to unauthorized access and data leakage. As such, development environments must have the same security controls as production environments.
What is DSPM?
DSPM tools monitor, manage, and mitigate data security risks across complex infrastructures. They are extremely valuable for organizations seeking to secure AI infrastructure because they provide continuous visibility into data locations, usage, and access. Security teams can use DSPM tools to discover and classify data, monitor for compliance, enforce security policies, and detect potential threats.
How DSPM Tools Protect AI Infrastructure
Now we understand what DSPM is, we can explore some of its capabilities as they relate to some of the key threats to AI infrastructure:
- Data Discovery and Classification: DSPM tools identify and categorize sensitive data throughout the AI environment, ensuring that data types like personally identifiable information (PII) or health records help organizations enforce stricter access controls on sensitive data and apply proper protections.
- Access Management and Prevention of Unauthorized Exposure: DSPM tools monitor data access in real-time and enforce role-based access controls (RBAC), thus allowing only authorized personnel to access datasets.
- Compliance Enforcement: DSPM tools simplify regulatory compliance by continuously assessing data handling practices against relevant regulations.
- Threat Detection and Data Integrity: DSPM tools help detect data poisoning or manipulation attempts by monitoring AI data pipelines and alerting teams to unusual or anomalous patterns.
- Applying Uniform Security Policies: DSPM tools apply the same security policies and controls to both development and production environments and flag any inconsistencies to security teams.
Future-Proof Your AI: Beyond DSPM
Although it’s well-trodden ground, it’s worth noting here that AI will define the future of business and productivity more broadly. Although we’re not quite there yet, in the coming years, almost all organizations will eventually use AI to some extent.
With this fact in mind, it’s crucial that we don’t get ahead of ourselves and, in the rush to gain a competitive advantage and develop AI infrastructure, ignore the massive legal and ethical considerations that come with this technology.
While DSPM is an essential tool for protecting AI infrastructure, it will not make up for irresponsible development or data scraping practices. As we’ve seen with the Clearview AI case, protecting data means little if you use data you’re not entitled to.
To ensure that you develop your AI infrastructure legally and ethically, you can look to recent regulations, like the EU AI Act, for guidance. These regulations will help you ensure your AI infrastructure meets the necessary requirements to ensure a safe and innovative future.
