Question 1

What is the enterprise risk of using agentic AI workflows like GitHub Copilot?

Accepted Answer

Agentic AI workflows, while powerful, introduce new attack vectors. The primary enterprise risks include: 1) Data Exfiltration: Malicious actors can use prompt injection to trick AI agents into accessing and leaking sensitive corporate data. 2) Code Injection & Sabotage: Agents can be manipulated into writing insecure code, introducing vulnerabilities, or sabotaging the codebase. 3) Tool Poisoning: If an AI agent uses external tools (APIs, databases), those tools can be compromised, leading to a supply chain attack. CodeIntegrity provides real-time guardrails to mitigate these specific risks.

Question 2

How does CodeIntegrity address the 'lethal trifecta' of AI security threats?

Accepted Answer

The 'lethal trifecta' consists of control-flow hijacking, data-flow corruption, and tool poisoning. CodeIntegrity uses a multi-layered defense: 1) For Control-Flow, we use semantic analysis to detect and block malicious instructions in prompts. 2) For Data-Flow, we sanitize and validate data retrieved by agents to prevent context poisoning. 3) For Tool Poisoning, we secure the Model Context Protocol (MCP) by validating tool calls and responses, ensuring the integrity of the agent's operational environment.

Question 3

Can CodeIntegrity be deployed on-premise for maximum data privacy?

Accepted Answer

Yes. CodeIntegrity is designed for enterprise security and can be deployed entirely on-premise or in your private cloud (VPC). This ensures that your source code, prompts, and sensitive data never leave your controlled environment, meeting strict compliance requirements like SOC 2, GDPR, and CCPA.

Question 4

How do your specialized Small Language Models (SLMs) improve security?

Accepted Answer

Instead of relying on a single large language model, we use a collection of fine-tuned SLMs, each specialized for a specific security task (e.g., prompt injection detection, PII redaction, hallucination detection). This approach provides higher accuracy, significantly lower latency (sub-millisecond), and a smaller attack surface compared to monolithic models. It allows for a more robust and efficient defense-in-depth security posture.

Question 5

What is the Model Context Protocol (MCP) and how do you secure it?

Accepted Answer

The Model Context Protocol (MCP) is a specification for how AI models interact with external tools and data sources. It's a critical component of agentic AI. We secure MCP by implementing guardrails directly at the protocol level, inspecting every tool call and response for anomalies, unauthorized actions, and malicious payloads. This prevents attackers from exploiting tool interactions, a common vector for advanced attacks.

Question 6

Does CodeIntegrity help with AI governance and compliance?

Accepted Answer

Absolutely. Our platform provides comprehensive logging, auditing, and policy enforcement capabilities for all AI interactions. This gives CISOs and compliance officers full visibility into how agentic AI is being used, helping to enforce security policies and generate auditable logs for compliance frameworks like NIST AI RMF and ISO/IEC 27001.

Application	Access	Compliance	Risk
ChatGPT	PII	78/100 3 issues	Medium
Claude	None	92/100 1 issues	Low
GitHub Copilot	None	88/100 2 issues	Low
Gemini	Secrets	74/100 4 issues	Medium
Cursor	None	85/100 0 issues	Low
DeepSeek	PIISecrets	56/100 7 issues	High

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