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CharliAI and Protegrity partner to accelerate AI agents through risk review and into production. October 20, 2026 CharliAI and Protegrity today announced a partnership designed to help regulated enterprises safely run AI agents on their most sensitive data. Protegrity safeguards the data agents access and the knowledge they may derive from it, while CharliAI controls what agents are authorized to do and maintains a forensic record of their actions. AI agents are moving out of pilots and into production workflows where they access critical data, change records and trigger actions in other systems. Security and audit teams must answer for every step, and most aren't ready to sign off. In EMA's 2026 State of AI Friction research, 83% of IT and security leaders report that security or compliance reviews have delayed AI from reaching production. Meanwhile, 69% already run agents that write to databases and trigger external APIs, yet still rely on controls built for humans querying data, not agents acting on it. "Organizations aren't short on AI ambition. They're short on a way through the risk review," said Christopher Steffen, VP of Research at Enterprise Management Associates (EMA). "Slow down for the review and you pay in delayed, diminished AI. Speed up and you pay in exposure. That's the AI friction tax, and most organizations are paying both sides of it. The way out isn't a faster review. It's protection built into the workflow, so agents can be cleared to do the work they were built for." Getting an agent through review comes down to two questions: what is it allowed to see, and what is it allowed to do? Agents reason, hand work to other agents and act at machine speed, so a one-time access check can't answer either question, nor can a dashboard that reports exposure after the fact. Answering both questions, while agents reason and act, takes data protection and runtime control working together. "The market is finally waking up from the AI hype cycle and realizing that capability is not clearance," said Kevin Collins, CEO of CharliAI. "Just because an agent can execute a complex workflow doesn't mean it has the authority to do so unsupervised. Enterprises want the productivity gains AI promises, but they cannot afford to hand over the keys to their data and systems to get it. We give them the runtime control and forensic proof they need to move autonomous agents into production." The integration embeds protection into the agent's workflow rather than at the perimeter. * Protegrity protects sensitive data before any model, agent or tool receives it. Agents work with semantically protected values that keep their meaning but not their risk, and policy decides what data to release based on who is asking and why. * CharliAI checks each agent's identity and authority before it acts and stops execution when behavior goes out of bounds. Decisions made and actions performed are recorded as the work happens, giving security and audit teams evidence of what the agent was shown and what it did. "The agent that should worry you isn't just the one that goes rogue. It's the one that does exactly what it's allowed to do and works out things nobody meant it to know," said Michael Howard, CEO of Protegrity. "Every agent is a new insider, one that never signed an NDA. Protecting your data and your knowledge means when an agent does something unexpected, it's an anomaly, not an exposure. Pair that with control over what agents do, and you get knowledge arbitrage: the most value from your data, with the least exposure." Through this partnership, banks, capital markets firms and insurers can purchase the integration from CharliAI, which resells Protegrity's data and AI security solutions alongside CharliAI's Ancaeus platform. DRJ HOT ITEMS BC/DR rules & regulations. BC glossary. Webinar Spotlight

How Protegrity and Composio secure Agentic AI workflows. By Muneeb Hasan, Senior Partner and Alliances Solution Engineer Jul 7, 2026 Share: The enterprise rush to adopt agentic AI is hitting a massive roadblock: data security. While standard Large Language Models (LLMs) can answer simple prompts, true enterprise value lies in AI agents - autonomous systems that can actively read, reason, and write across your corporate software ecosystem. But giving an AI agent the "hands" to access Salesforce, Jira, Slack, or any other platform introduces unprecedented risk. How do you leverage the reasoning power of an AI agent without exposing your most sensitive corporate assets - PII, PHI, and PCI - to the LLM or third-party platforms? The answer lies in a groundbreaking architectural integration between Composio, the AI agent orchestration platform, and Protegrity, the global leader in data protection. Together, they introduce the "Privacy Sandwich" - a design pattern that enables a true Zero Trust AI workflow. The business value: unleashing AI productivity without the risk. For CXOs and security leaders, this integration solves the ultimate AI paradox: balancing aggressive innovation with strict regulatory compliance, including GDPR and HIPAA. By embedding Protegrity's robust data protection directly into Composio's flexible agentic workflows, enterprises can realize three core business benefits: Zero Trust AI reasoning. The core philosophy of this integration is simple: an LLM does not need to know a customer's real name or credit card number to understand their intent. Protegrity sanitizes data by finding and redacting sensitive PII, PCI, PHI, and other protected information. By feeding the AI sanitized, format-preserving semantic protection instead of raw data, your business can securely use public, private, or hybrid LLMs. Elimination of third-party data leakage. When AI agents leverage Composio to connect with external applications, there is a constant risk of data exposure. The "Privacy Sandwich" ensures that data is neutralized by applying Protegrity's data protection before it leaves your secure perimeter. Even if an agent interacts with an external cloud service, it passes secure semantic protection rather than sensitive data. Dynamic, identity-aware compliance. Not all human users are created equal in the eyes of compliance. When an AI agent formulates a response, Protegrity automatically evaluates the role and privileges of the specific human interacting with the agent. An HR manager can see unprotected data they are authorized to view, while a general support agent sees semantically protected data - all from the same AI workflow. The technical blueprint: how it works under the hood. Achieving this level of security requires tightly coordinated choreography between Composio's orchestration capabilities and Protegrity's centralized policy engine. [External Sources] | (Composio Ingestion) | [Protegrity Gateway] | (Sanitized Data) | [LLM Processing] | [Authorized Human] | (Identity Check) | [Protegrity Gateway] | (Tokenized Output) | + The process spans three fundamental technical phases: Phase 1: Ingestion and initial protection - The inbound shield. * Multi-source orchestration: Composio acts as the "hands" of the enterprise, triggering APIs to gather raw datasets from platforms like Salesforce or Jira. * Automated discovery: Before this raw payload touches the LLM, it passes through the Protegrity Gateway, which scans unstructured text to automatically identify sensitive entities. * Format-preserving tokenization: Protegrity replaces sensitive elements with format-preserving tokens. For example, John Doe becomes Person_Token_882. The LLM receives an entirely sanitized context. Phase 2: secure reasoning and secondary action. * Context preservation: The LLM processes the request using tokenized data. If it decides to execute a secondary task, such as "Update ticket for User_Token_123," Composio passes that exact token to maintain context across systems. * Intermediate validation: If Composio's secondary actions pull new data into the conversation loop, the Protegrity Gateway immediately intercepts, discovers, and sanitizes it before the LLM processes it again. Phase 3: egress and role-based access control. * Response interception: The LLM generates its final output, which still contains semantic protection. * Dynamic de-identification: The Protegrity Gateway intercepts the outbound message, queries Protegrity Policy Management, and checks the user's active directory role. The gateway either dynamically semantically unprotects the data for authorized users or leaves it masked for unauthorized users. Advanced deployment: the vault approach. For highly strict regulatory environments, enterprises can opt for the Local Execution Pattern. Rather than relying on hosted cloud infrastructure, the Composio Local Runner is deployed directly within your private network. It hooks directly into your local data vault, ensuring PII is only unmasked in memory at the final moment before being pushed to a secure API destination. Conclusion. The future of enterprise productivity belongs to autonomous AI agents, but innovation cannot come at the cost of security. By combining Composio's powerful integration fabric with Protegrity's Zero Trust data protection, organizations no longer have to compromise. Are you ready to safely accelerate your agentic AI roadmap? Reach out to learn more about implementing the Protegrity and Composio integration in your environment. Frequently asked questions. What is Agentic AI, and why does it introduce new data security risks? How does the Protegrity and Composio integration ensure Zero Trust AI processing? What is format-preserving tokenization in the context of LLM workflows? Can different users see different data outputs from the same AI agent workflow? How do Protegrity and Composio support strict local or on-premises data compliance? Summary. * Protect Data Before the LLM Protegrity and Composio use a "Privacy Sandwich" architecture to sanitize sensitive data before AI processing and enforce policy before output. * Use AI Agents With Less Data Exposure Teams can connect agents to enterprise tools while reducing exposure of PII, PHI, and PCI through tokenization, semantic protection, and role-aware controls. Build safer AI with protected data. Get practical insights on securing sensitive data across AI pipelines, analytics workflows, GenAI systems, and regulated environments. This site is protected by reCAPTCHA. Recommended next read. Blogs. 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