AI Security & Governance

The adoption of AI has accelerated over the years. AI is now part of an organization’s many processes and is used in various forms. Many have developed their own AI applications, also called Sanctioned AIs, while others have leveraged the powerful capabilities of AI agents like Copilots, embedded within SaaS platforms.

Regardless, organizations must ensure that their AI systems are secured, using a robust data + AI security and governance framework. Without such a framework, even the most mature organizations may be exposed to significant security and compliance risks.

The potential of AI cannot be fully leveraged without strong data and AI controls in place. In fact, Morgan Stanley reports that 64% of enterprises cite a lack of data security preparedness as the reason behind the slow adoption of genAI. Apart from that, AI systems are also vulnerable to unprecedented AI risks, as highlighted by OWASP in its Top 10 LLM risks report. These risks include Prompt Injections, Sensitive Information Disclosure, Misinformation, and Excessive Agency, to name a few.

The most concerning aspect of these risks is that they are unique to AI. Unlike traditional systems and infrastructures, LLMs or AI models are highly dynamic in nature and constantly evolving. The evolution of these intelligent systems is based on the data they are exposed to and interactions with it. Moreover, the risks OWASP highlighted aren’t just strictly limited to the AI models themselves but related to data. This is all the more reason why an integrated data and AI security is a must for protecting AI systems.

Here, a comprehensive DSPM solution that extends its offerings to AI security and governance is necessary to secure the data that is powering AI systems and applications.

A robust DSPM with built-in AI security capabilities can help organizations:

• Discover and catalog sanctioned and unsanctioned AI models automatically, along with copilots that are implemented across hybrid, multiclouds and SaaS applications.
• Get complete context of AI models and agents, which includes information like data ownership, access entitlements, usage, and misconfigurations, etc.
• Assess risks associated with AI using industry benchmarks and model cards.
• Classify models according to global data and AI regulations.
• Map AI models to associated data, processing activities, vendors, and risks.
• Establish effective data controls for AI models and for data that AI models generate. These controls may include data sanitization, redaction, anonymization, masking, and LLM firewalls.
• Automate compliance for controls associated with global regulations and frameworks like GDPR, EU AI Act, NIST AI RMF, etc.

LLM firewalls are advanced technical controls that have become prominent as the popularity and demand of GenAI adoption have increased. Unlike web application firewalls that filter malicious network traffic, LLM firewalls are placed in a GenAI pipeline to protect AI systems and applications against different attacks. DSPM solutions with integrated LLM firewalls are customized to the unique architecture of AI applications, helping organizations prevent attacks like prompt injection. The firewall inspects prompts, LLM’s response to the query generated by users, and the data retrieved from RAG pipelines to ensure data security and privacy. The prompt, response, and retrieval firewalls are built to detect even multilingual prompt attacks and various content. It does so by integrating inline LLM security controls.

There can be numerous examples of LLM firewall policies that security teams can create to prevent various AI attacks. For instance:

• Users can prevent jailbreaking by blocking the malicious prompt.
• Semantic analysis can be leveraged to block malicious prompts and thus prevent prompt injection attacks.
• Users can also block responses that contain negative sentiments like hate or anger.

AI is now one of the greatest means of future innovation and advancements. Hence, to ensure secure and safe AI adoption, organizations must extend their security posture to AI security and governance.