Drive Responsible AI Adoption through Unified Trust Layer
Unlock the full potential of AI while maintaining reliability, compliance, and security.
Enterprises are building AI agents — without the infrastructure to trust them.
Enterprises are already moving beyond chatbots to deploy agents and workflows that take action, make decisions, and automate business processes. But this new generation of AI systems is being built on shaky ground — without reliability, governance, and security needed to operate responsibly at scale.
Common Challenges
No Visibility
As AI agents proliferate across teams and tools, enterprise stakeholders — from IT to compliance — lack answers to fundamental questions:
- What agents exist?
- What models and data power them?
- Who is triggering them and how often?
- What are they producing and where do outputs go?
Without visibility, these gaps create serious risks, from operational inefficiencies and missed opportunities to security breaches. Gartner predicts that by 2027, 40% of data breaches will come from AI misuse, underscoring the need for oversight.
No Reliability
Unlike static models, AI agents take action. They synthesize data, reason across tasks, and generate results that influence real-world outcomes. But without enterprise grounding, agents:
- Hallucinate or fabricate results
- Produce inconsistent or contradictory outputs, especially when using structured sources like SQL, think Text2SQL.
- Break workflows with brittle prompt chains
- Operate outside their intended purpose
Unreliable agents can mislead teams, disrupt processes, and degrade trust, leading to wasted resources and lost productivity.
No Guardrails
AI agents often bypass traditional access controls due to their dynamic, autonomous nature and their ability to operate with broad access to systems. They interact with sensitive data, trigger downstream systems, and generate action all without:
- Role- or attribute-based access enforcement
- Purpose boundaries & binding (e.g., “marketing” vs “compliance” usage)
- Real-time policy enforcement at inference
Without guardrails, agents become a vector for data leakage and unintended automation potentially exposing organizations to security breaches and compliance violations.
Trustworthy AI for Enterprise Success
Enterprises need to invest in the three pillars of trust to overcome the challenges posed by unreliable AI and lack of visibility:
- Reliability – Ensuring AI systems deliver accurate, efficient, and consistent results is key to preventing errors, hallucinations, and inconsistent outputs that undermine trust and disrupt business processes
- Governance – Strong governance provides accountability, policy enforcement, and transparency to ensure AI systems align with organizational goals and operate within ethical boundaries, reducing risks like Shadow AI and unauthorized access
- Security – Robust access controls, protection, and real-time monitoring are critical to safeguard sensitive data, prevent data breaches, and ensure AI agents comply with security protocols, mitigating potential vulnerabilities and legal risks.
Platform
The First Unified Trust Layer for Generative AI
Trust3 AI is the first AI-native, intelligent, and adaptive trust layer designed to drive responsible, reliable and secure Generative AI in the organization. Trust3 AI brings visibility to AI agents, MCP Servers, RAG workflows, models, AI tools and any Gen AI system to effectively measure and manage any AI risks including safety, security, data compliance, accuracy and operational risks.
With Trust3 AI, you can:
- Discover and catalog AI agents and applications
- Measure performance, reliability, and data compliance across your Gen AI systems & applications.
- Govern behavior with real-time guardrails and responsible agent oversight
Build trust. Accelerate adoption. Scale safely.
