Note from Lionel: Future Insights 2025 is a blog series where we asked our senior security leaders what they see as the future of the cybersecurity industry. We kick off the 2025 series with Jaimen Hoopes, GM of Data Security & VP of Product Management, on the topic of AI Security Posture Management (AISPM). Click to read other posts in the series

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Effective data security requires more than simply putting measures in place to block the exfiltration of sensitive information; it also calls for gaining an accurate understanding of what and where data is in the first place and weeding out ROT (Redundant, Obsolete and Trivial) data. That’s why in addition to our celebrated Data Loss Prevention (DLP) capabilities, Forcepoint offers a Data Security Posture Management (DSPM) solution. This takes a proactive approach to security by discovering, classifying and orchestrating data while employing automation to rapidly detect emerging risk factors.

In today's rapidly evolving technological landscape, artificial intelligence (AI) has become an integral component of many enterprise operations. From enhancing customer experiences to streamlining internal processes, AI tools are driving significant advancements across industries. However, with great power comes great responsibility, and it's crucial for businesses to understand the risks associated with AI and how to manage them effectively. This is where AI Security Posture Management (AISPM) comes into play.

The importance of understanding AI risks

AI systems, while transformative, introduce a unique set of risks that differ significantly from traditional IT threats. These risks can have profound implications for an enterprise, ranging from data breaches to operational disruptions. Understanding these risks is essential for safeguarding your business's assets, reputation and overall functionality.

Data Privacy and Security: AI tools often require vast amounts of data to function effectively. This data can include sensitive information, and any breach or misuse could lead to severe privacy violations and legal consequences.
Model Vulnerabilities: AI models are not immune to attacks. Adversarial attacks, where malicious actors manipulate input data to deceive AI systems, can lead to incorrect outputs and decisions, potentially causing significant harm.
Bias and Fairness: AI systems can inadvertently perpetuate or amplify biases present in training data. This can result in unfair treatment of individuals or groups, leading to ethical and legal concerns.
Operational Risks: AI systems can malfunction or produce unexpected outcomes due to flaws in the model or changes in data patterns, potentially disrupting business operations.

What exactly is AISPM, and what do you do with it?

Whereas CSPM focuses on cloud infrastructure and DSPM encompasses data in all stages of its lifecycle, AISPM focuses on the security of AI and Machine Learning (ML) systems wherever they are used in your organization.

AISPM is tailored to addressing the vulnerabilities associated with AI systems and how they interact with data and infrastructure. Key AISPM capabilities that you can use to solve AI security problems include:

AI Discovery: To gain visibility on which AI tools are being used, including shadow AI, start with a thorough assessment of the AI tools in use. Identify potential risks related to data, model integrity and operational impact. Regularly update this assessment as the technology and threat landscape evolve. Leverage tools that can help identify shadow IT AI tools that may be used by individuals or teams throughout the organization without having been fully sanctioned by your corporation.
Misconfiguration Detection: Use this to identify misconfigured AI services and apply configuration rules.
Data Governance: Implement robust data governance practices to ensure data used for training and operational purposes is secure, accurate and compliant with privacy regulations. This includes data encryption, access controls and regular audits.
Attack Path Remediation: Identify and eliminate vulnerabilities and avoid sensitive information mixing with training data.
Compliance Enforcement: Ensure that all AI use is conducted according to applicable regulatory requirements, from configuration to reporting.
Ongoing Monitoring and Improvement: AI security is not a one-time task but an ongoing process. Continuously monitor AI systems for emerging threats and vulnerabilities and adapt your security measures accordingly.

As AISPM gains in popularity as a solution category – and I fully expect that it will continue to do so – we may see the emergence of a more detailed and standardized set of capabilities that every AISPM vendor will offer as table stakes.

What makes Forcepoint a major player for AISPM?

Forcepoint is well positioned to take a leading part in the AISPM conversation, with our specialization in data security that from the beginning has been heavily focused on countering the emerging threats associated with generative AI. The AI Mesh model that currently powers classification for our DSPM solution can radically improve the accuracy with which organizations identify sensitive data and guard it from exfiltration via AI systems.

In the video below, I provide an overview of what differentiates AI Mesh and makes it so transparent and efficient. At the heart of this performance is the generative AI Small Language Model (SLM), which requires far less computing power and is therefore faster and more easily customizable than an LLM.

Forcepoint DLP

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Jaimen Hoopes