AI SPM: Artificial Intelligence Security Posture Management

Artificial intelligence usage is on an upward trend. Look at AI trends—whether talking about its growth, annual spending on businesses, or even use by individuals—and the graph consistently shows numbers increasing, year after year.

In 2024, the AI market size was over $500 billion, but it’s supposed to be over $2,500 billion by 2032. AI is here to stay, and businesses are not only adopting it but are actively investing in its development. If your organization is one of them, you need to think about AI security posture management (or AI SPM).

What Is AI SPM?

Artificial intelligence is a blanket term that covers a number of different technologies, including machine learning (ML), deep learning, natural language processing (NLP), speech recognition, image recognition, and more. These are built on AI models, data pipelines, and infrastructure to support the AI system.

AI SPM, or AI security posture management, is a cybersecurity strategy that helps keep these AI components safe from threats, both internal and external. The strategy relies on continuous monitoring, assessing, and mitigating risks, like data breaches and adversarial attacks. At the same time, it also makes sure the system complies with relevant regulations, such as the National Institute of Standards and Technology (NIST) or General Data Protection Regulation (GDPR).

The reason why you need a specific strategy for your AI systems is because this technology comes with certain unique risks that traditional cybersecurity measures might not be able to address.

Risks Introduced by AI

While artificial intelligence does help streamline your business operations, it also has certain weaknesses that can be exploited. Understanding these can help you develop a comprehensive AI SPM plan.

Privacy and Data Security

We’ve discussed AI privacy concerns in detail, but the gist is that you need large volumes of data—both structured and unstructured—to train AI models. This data is usually pre-collected information for other purposes, which might make consent a concern. It is also a privacy risk if not managed properly.

Moreover, threat actors can exfiltrate this sensitive proprietary information by targeting GenAI tools, databases, and application programming interfaces (API). There’s also a risk that this information could be revealed through unintentional negligence by your organization or improper configurations that result in the data being exposed.

AI-Powered Attacks

Your business isn’t the only one using AI to scale and optimize your operations; cybercriminals are using it too. They’re using GenAI to automate attacks and make them more personalized.

They may also use this technology to create deep fakes—artificially generated images and videos—or fake biometrics to infiltrate your AI infrastructure and applications. Fake biometrics can also be used to gain access to your software development kits (SDK) or APIs, allowing threat actors to escalate attacks or get into your enterprise cloud environments.

Misinformation

As we know, an AI system is as good—and accurate—as the data it is trained on. If the model doesn’t have adequate information in its training data, it will go on to hallucinate answers. And, if threat actors manage to break into the training data to manipulate or corrupt it, your large language model (LLM) might give out wrong or dangerous information.

Lack of Data Visibility

Knowing where your data is, how it’s being protected and used, and how it’s being destroyed after you’ve used it is an important part of data privacy compliance. As we mentioned before, AI models need a lot of data for training. If you don’t have an AI data inventory, there’s a risk of shadow data (untracked or unmanaged datasets), compliance violations, and data breaches that aren’t discovered until it’s too late.

Shadow data models—unauthorized AI systems that operate outside your governance frameworks—can be risky as they might use unvetted or improperly secured datasets, which increases the risk of data poisoning attacks. Again, if that happens, you’re at risk of compliance violations and penalties in addition to the reputation damage you might face.

Data Governance

Since AI data is potentially at risk and a potential risk to others, governments are creating strict laws to govern it. AI governance especially focuses on sensitive personal data and personally identifiable information (PII), which are highly susceptible to exposure. As these regulations evolve, businesses need to strengthen how they manage their AI systems to prevent fines and legal action.

Complicated Supply Chain

Building an AI system relies on a complex supply chain of components. Each model is powered by source data, reference data, libraries, APIs, and pipelines. All of these components come with a risk of vulnerabilities or misconfigurations that can be exploited by threat actors. This complex ecosystem needs proper oversight or it can become a liability for your business.

Runtime Misuse

AI systems, especially LLMs, are prone to exploitation or inappropriate use during their operation. If you don’t have proper safeguards, you risk:

• Prompt overloading: Overloading the system with complex or malicious inputs that cause it to perform unpredictably or give out unauthorized outputs.
• Adversarial inputs: Using carefully crafted inputs to exploit weaknesses in the model, and causing it to give out wrong or harmful answers or misclassify objects.
• Unauthorized access: Exploiting vulnerabilities in the runtime environment to manipulate or gain access into the AI system.
• Sensitive data extraction: Manipulating the system’s inputs and interactions to get it to reveal sensitive information from improperly sanitized training data.

The Benefits of AI SPM

Now that we are familiar with the risks of AI, we can understand how traditional cybersecurity strategies might not be as effective here. This is where AI SPM can help manage and mitigate risks from every stage of the AI lifecycle and supply chain.

It can proactively manage vulnerabilities and misconfigurations in the AI pipelines, training data, and runtime environments before they can cause issues. AI SPM will also monitor your systems for runtime misuse, flagging any abnormal activity quickly before it becomes a problem.

This strategy also helps you gain visibility into your datasets, preventing shadow data and keeping you compliant with regulations. Most importantly, it gives you the confidence to adopt AI and innovate with it confidently.

AI SPM vs CSPM vs DSPM vs SSPM

There are a few “security posture management” types floating around. How do they differ from AI SPM?

As we’ve seen, AI security posture management refers to the strategy of keeping AI systems secure by constantly monitoring, assessing, and mitigating risks. DSPM, or data security posture management, is the process and framework that keeps an organization’s data—no matter where it resides—secure.

CSPM, or cloud security posture management, on the other hand, only deals with data residing in the cloud and configurations that could lead to information being compromised.

Finally, SSPM, which stands for SaaS security posture management, is all about protecting business data that’s contained within SaaS applications that your organization uses.

Features and Capabilities of AI SPM

AI Inventory Management

Not having visibility of your AI data can be a problem and is one of the risks created by the use of AI. SPM for AI can help you solve this by keeping track of not just your data but also other AI assets such as models, pipelines, and shadow AI systems.

Data Security

This one is right there in the name—security posture management. One of the most important capabilities of AI SPM is identifying sensitive information, like PII or PHI (personal health information) in your datasets and logs and securing it. It keeps all your data safe from threats like data poisoning and breaches, including shadow data.

Operational Security

AI SPM isn’t a one-and-done process. It secures your AI systems across their lifecycles, from development to deployment. The strategy protects model supply chains by checking dependencies and not allowing unauthorized changes. You can also implement measures to prevent the theft of proprietary AI assets with countermeasures for model extraction.

Risk Detection and Prioritization

If there are any misconfigurations, such as exposed APIs, weak encryption, or insecure logging, AI SPM will detect them. It will also identify potential attack points and paths, and assign them risk scores, so you can prioritize your remediation efforts.

Runtime Monitoring

Since AI models are so susceptible to attacks during use, having real-time monitoring capabilities in your AI SPM is a huge advantage. It keeps an eye out for behavioral anomalies, flags unauthorized access, and prevents adversarial attacks. It also scans outputs and logs to find out if there are any sensitive data leaks or suspicious behavior.

Compliance and Governance

With AI SPM, you can meet the requirements set by AI governance frameworks and legislations. You can use it to provide audit trails for your model development and approvals, and embed privacy and security policies in your workflows. Since it automatically detects and corrects potential violations, you can stay on the right side of the law quite effortlessly.

Proactive Remediation

As we’ve seen, AI SPM monitors your AI systems constantly and in real time. As a result, you can catch errors and potential threats early, before they lead to bigger issues.

Developer-Friendly Features

Tools like role-based access control (RBAC) and risk triage allow your developers and data scientists to manage and address vulnerabilities efficiently. They also facilitate collaboration and make resolving critical risks easier.

Scalability and Cloud integration

AI SPM tools can integrate with cloud platforms, and their multi-cloud compatibility allows them to support diverse environments and frameworks.

Protect Your AI Environment With BigID

While AI does create excellent growth opportunities through innovation for your business, it admittedly comes with some unique challenges. Fortunately, you can mitigate most of them with AI SPM. You can also use BigID’s data mapping, security, and governance abilities to bolster its protection.

Why BigID for AI Security?

• Comprehensive Data Discovery: Automatically identify and classify sensitive information, such as PII, customer data, intellectual property, and more, across your entire data landscape. Gain visibility into your data to prevent misuse in AI models or LLMs.
• Enhanced AI Governance Compliance: Align your operations with emerging regulations like the AI Executive Order and Secure AI Development Guidelines, ensuring the responsible and ethical use of AI through BigID’s secure-by-design approach.
• Optimized Data Management: Minimize redundant or duplicate data to improve the quality of AI training datasets, reduce your attack surface, and enhance overall security posture.
• Secure Data Access: Mitigate insider threats by managing, auditing, and restricting access to sensitive data, preventing unauthorized use in AI systems.

To protect your AI data and systems, schedule a free 1:1 demo with BigID today.