Leading LLM Security Companies

As generative AI (GenAI) systems become critical in enterprise workflows, the need for robust, real-time security grows. These large language models (LLMs) process vast quantities of sensitive information, from customer records to proprietary algorithms. With threats ranging from prompt injection to data leakage, organizations are turning to advanced platforms that specialize in LLM observability, audit, masking, and compliance. In this landscape, several vendors stand out. This article explores the Leading LLM Security Companies and how they empower secure GenAI adoption through core capabilities like real-time audit, dynamic masking, and automated compliance enforcement.

Securing GenAI: The Technical Foundations

Security for LLMs goes beyond encrypting storage or enabling basic logging. A secure GenAI pipeline demands full-stack visibility—from the prompt input to the final LLM response—along with inline policy enforcement and data classification. This includes:

• Real-time audit of user interactions and LLM decisions
• Dynamic masking of PII/PHI during inference
• Data discovery and classification before model ingestion
• Access policies that evolve with usage patterns
• Compliance workflows mapped to standards like GDPR or HIPAA

According to MIT Sloan, a key best practice is treating LLMs as both risk surfaces and intelligent endpoints.

Let’s look at how the top security vendors in this space deliver on these needs.

DataSunrise: Granular Policies for LLM and Database Pipelines

Originally focused on database security, DataSunrise has extended its capabilities to support GenAI security. Its real-time audit engine allows organizations to track LLM queries issued via embedded SQL or prompt-connected APIs. Masking is applied dynamically based on user role and rule context, using flexible dynamic masking rules. These policies adapt based on behavior analysis and access context.

Data discovery is also a key strength. The platform automatically scans structured and semi-structured data stores to identify PII or sensitive fields before they reach model training or inference stages. Combined with security policies and audit storage optimization techniques, DataSunrise ensures GenAI deployments remain verifiable, masked, and compliant in production environments.

-- Example: Apply dynamic masking on a column accessed by an LLM
CREATE MASKING RULE mask_llm_sensitive_data
ON schema.customer_data (ssn, email)
WHEN ROLE IN ('llm_user', 'analyst')
USING 'XXXX-XX-####', '***@example.com';

Securiti.ai: Autonomous Data Intelligence for GenAI

Securiti delivers an AI-powered data control cloud platform that automates discovery, classification, and access governance across hybrid environments. Its solutions are increasingly used in GenAI pipelines where developers fine-tune models on enterprise data or plug LLMs into analytics workflows.

Real-time compliance alerts, classification-backed masking, and AI-governed access policies are key highlights. Securiti’s integration with cloud-native tools like Snowflake, Azure SQL, and Cosmos DB ensures seamless enforcement of GDPR and HIPAA obligations from inference to storage.

The platform was recently recognized in Forrester Wave for Privacy Management Software for its leadership in automated policy enforcement.

Duality Technologies: Privacy-Preserving GenAI Collaboration

Duality focuses on secure data collaboration and AI training via homomorphic encryption and secure multiparty computation. While not a full-spectrum LLM protection tool, Duality enables use cases where GenAI is trained on joint datasets without exposing raw information. This is crucial for financial services or healthcare sectors where compliance with PCI-DSS or data localization laws is strict.

IDC reports that federated learning and privacy-preserving computation are gaining traction across AI governance initiatives, with Duality being a top vendor.

HiddenLayer: ML-Specific Threat Detection

HiddenLayer offers a security platform tailored to machine learning systems. For organizations deploying LLMs, it provides protection against adversarial prompts, model extraction, and evasion attacks. The platform’s runtime monitoring layer can detect anomalies in GenAI outputs or access behavior and trigger automated defenses.

Although HiddenLayer does not natively perform data discovery or masking, its niche focus on the security of AI models during and after training makes it a strong candidate for high-risk environments. Its detection engine aligns with the NIST AI Risk Management Framework, enhancing model assurance practices.

Protecting the Future: Best Practices Beyond the Tools

Choosing the right platform is critical, but securing GenAI also requires well-defined operational practices. A few takeaways:

• Always scan and classify input data before training
• Enable auditing at every prompt invocation point
• Use data-inspired security to define adaptive masking
• Align LLM logs with compliance policies and retention timelines

Further reading from Google DeepMind outlines why fine-tuning, guardrails, and continuous audit loops are essential to LLM safety in high-stakes domains.

Security teams must treat LLMs as dynamic data consumers and generators—requiring not just perimeter defenses, but deep observability and fine-grained access enforcement.

Conclusion

As LLM adoption accelerates, enterprises must adapt their security stack to new risks and regulatory pressures. The Leading LLM Security Companies showcased here—DataSunrise, Securiti.ai, Duality, and HiddenLayer—bring different strengths to the table. Whether it’s audit trails, masking, encrypted collaboration, or threat detection, each provides essential safeguards tailored for GenAI workloads. A layered approach that combines these capabilities is the most effective way to secure generative AI in enterprise environments.