Technology Comparison

Cequence AI Gateway vs.
Claude, ChatGPT and Copilot Native Integrations

Claude, ChatGPT, and Copilot are all building native connectors to enterprise tools, and each one governs those connectors its own way — its own identity model, its own audit trail, its own blind spots. None provides behavioral detection, agent-scoped personas, sensitive data inspection at the payload level, or a unified control plane across platforms.
Cequence AI Gateway sits underneath all of them. It governs what agents do with your enterprise applications and data regardless of which AI platform the agent runs on.

The Problem with Platform-Native Integrations

Every major AI platform is racing to connect agents to enterprise systems:
Claude connects to 6,000+ apps via MCP. 75+ official connectors including Slack, Asana, Box, Figma, Salesforce. Claude Code and Claude Desktop support MCP natively.
ChatGPT offers MCP access connectors and synced connectors for SharePoint, GitHub, Teams, Outlook, Google Drive, Dropbox, Box. Codex plugins directory launched March 2026 with packaged workflows and admin controls.
Copilot Studio provides multi-agent orchestration (GA April 2026), Microsoft 365 Graph API integration, Fabric data connectors, Power Platform governance. Deepest native integration within the Microsoft ecosystem.
Each platform is building its own walled garden. For an enterprise that uses more than one of them, and most do, the result is fragmented governance with no single point of control.

What Native Integrations Do Not Provide

No Unified Control Plane

When your engineering team uses Claude Code, your sales team uses ChatGPT, and your operations team uses Copilot, you have three separate governance models, three separate audit trails, and three separate identity configurations, each with its own uncovered corners. Cequence provides a single governance layer across all of them.

No Agent Personas

Native integrations control which connectors a user can enable. They do not control what an agent is allowed to do with those connectors at the tool-call level. Cequence Agent Personas define the intersection of user permissions and what the agent is explicitly allowed to do. A user with access to Salesforce, Snowflake, and Jira assigns a “Pipeline Review Agent” persona that permits read access to Salesforce opportunities only. The other tools are invisible. This works regardless of whether the agent runs on Claude, ChatGPT, or Copilot.

No Behavioral Detection

Native integrations authenticate and route. They do not analyze what agents do after authentication. An authorized agent that scrapes customer data through legitimate connector calls, exfiltrates PII through tool responses, or repeats failed operations across sessions will not trigger any alert in any native platform. Cequence provides behavioral analytics built on 10+ years of API attack pattern data.

No Sensitive Data Inspection

Native integrations pass data between the AI platform and your enterprise systems without inspecting payloads for sensitive data content. Cequence inspects MCP tool call payloads in real time, both requests and responses, with compliance-mapped detection policies. Block, redact, or alert.
That same real-time inspection applies to direct LLM prompts and responses through LLM Registry, including Prompt Guard detection of prompt injection, jailbreak attempts, and system-prompt extraction — one policy regardless of which platform the agent runs on.

No Protection When Agents Bypass Connectors

Claude Code writes scripts that call APIs directly. Copilot agents execute code that constructs HTTP requests. ChatGPT Codex generates code that hits endpoints natively. When an agent bypasses the native connector, the platform’s governance disappears. Cequence protects APIs regardless of how the request arrives.

Platform-by-Platform Comparison

Capability Claude ChatGPT Copilot Cequence AI Gateway
Enterprise connectors 75+ official MCP connectors MCP + synced (SharePoint, Teams, Drive) M365 Graph, Fabric, Power Platform No-code enterprise API to MCP. Auto discovery and cataloging.
Identity model Anthropic account, Team/Enterprise roles Workspace RBAC, OAuth per connector Entra ID, M365 RBAC OAuth 2.1, any IdP. Two-layer credential isolation.
Agent-scoped governance Connector enable/disable Workspace connector RBAC Power Platform DLP policies Agent Personas: per-user, per-tool, and per-model scoping, enforced automatically via policy.
Sensitive data No No Purview (separate product) Native. Compliance-mapped across tool calls, API requests, and LLM prompts and responses, including base64 and evasion-character detection. Block, redact, alert.
Prompt injection protection Platform-native filtering only. Platform-native filtering only. Platform-native filtering only. Prompt Guard detects prompt injection, jailbreak attempts, and system-prompt extraction, consistently across whichever platform the agent runs on.
Behavioral detection No No No Yes. Application abuse, anomalous patterns, exfiltration.
Cross-platform visibility Claude only ChatGPT only Microsoft only All platforms. Single control plane.
Shadow AI / LLM discovery No cross-platform discovery. No cross-platform discovery. No cross-platform discovery. AI Discovery surfaces every agent, LLM provider, and MCP server running across the enterprise from existing SIEM logs — whether it went through Claude, ChatGPT, Copilot, or an unofficial path.
Audit trails Conversation history Admin activity logs M365 audit log User-attributed, OpenTelemetry, SIEM-ready.
API protection (non-MCP) No No No Yes. API and MCP. 10+ years.
API registry No. Connector credentials held per connector. No. Connector credentials held per connector. No. Connector credentials held per connector. Brokers API credentials so agents never hold them directly, across all three platforms.
Skills registry No equivalent. No equivalent. No equivalent. Curated, vetted, reusable agent capabilities that security and platform teams approve once and reuse across every agent.

Agent Personas: Cross-Platform Least Privilege

Native integrations control which connectors a user can enable. They do not control what an agent does with those connectors at the tool-call level, and that difference matters more than it appears.
 
Prompt injection is an unsolved problem. The “Agents of Chaos” study (MIT, Harvard, Stanford, CMU, February 2026) compromised all six test agents through social engineering, not jailbreaks. Google DeepMind’s “AI Agent Traps” (April 2026) achieved 86% attack success rates. A March 2026 study argues prompt injection is a “pipeline-stage problem, not a model-capability problem.” No amount of model improvement will eliminate it.
 
If you cannot prevent an agent from being coerced, you must constrain what a coerced agent can do. That is what Agent Personas solve, and they work regardless of which AI platform the agent runs on.
 
A Persona is a job description for an AI agent: the intersection of user permissions and what the agent is explicitly allowed to do. Always a reduction, never an expansion. A user with access to Salesforce, Snowflake, and Jira assigns a “Pipeline Review Agent” persona that permits read access to Salesforce opportunities only. The other tools are invisible. A coerced agent that can only see one read-only tool cannot exfiltrate data from the other two, regardless of whether it runs on Claude, ChatGPT, or Copilot.
 
Behavioral monitoring adds the second layer. When an agent begins operating outside expected patterns, Cequence detects it. Together, personas and behavioral detection do not prevent prompt injection. They prevent it from causing material harm across any platform.

Case Study: When an Agent Goes Rogue to Get the Job Done

The Agents of Chaos study showed agents destroying infrastructure and leaking data in a lab. Here is what it looks like in production, where no native platform integration detected anything wrong.
Environment: Fortune 50 enterprise. Autonomous AI coding agent analyzing a legacy codebase. 47 continuous hours. 2,575 tool calls. Entirely unsupervised.
 
What the AI platform showed: 2,575 authenticated requests. Zero anomalies.
What actually happened: The agent got stuck and got creative. It guessed 162 filenames based on build conventions without checking a directory listing first. None existed. It hallucinated the tail end of 40-character commit hashes, mutating them repeatedly over 71-second loops before giving up and re-reading source data. It re-probed the same wrong paths across sessions spanning 27 hours because it has no memory between sessions. It self-corrected some errors in seconds but got stuck in others indefinitely.
This is not a malicious agent. It is a determined one. And determination without guardrails is the failure mode the research warns about.
What Cequence did: Reconstructed the full behavioral trail. Identified six error clusters. Produced targeted recommendations for improving the agent, all prompt-level changes, zero infrastructure work. Projected error reduction from 212 to under 20 per 48-hour window.
The native AI platform showed a clean session. Cequence showed the engineering team exactly where the agent was struggling, why, and how to fix it.

Why This Matters for Security Teams

The AI platform vendors are building connectors to drive adoption of their platforms. Their incentive is to make it easy for agents to access enterprise data. Your incentive is to make it governed, auditable, and safe. These incentives are not aligned.
Siloed. Each platform governs its own connectors. No cross-platform visibility.
 
Shallow. RBAC and OAuth at the connector level. No payload inspection. No behavioral detection.
 
Platform-locked. Policies in one console do not carry to another. New platform means starting over.
AI Discovery surfaces agents, LLM usage, and MCP servers across all platforms from existing SIEM data, whether or not they were provisioned through an official connector.
 
Incomplete. None protects APIs when agents bypass connectors and call endpoints directly.
Cequence is not a replacement for native integrations. It is the security and governance layer that sits underneath all of them.

Summary

Native AI platform integrations solve the connectivity problem. They make it easy for agents to reach enterprise tools. They do not solve the security problem.
 
The security problem is: who governs what agents do across all platforms, who inspects the data flowing through those connections, who detects when an agent behaves anomalously, and who enforces least privilege at the agent level rather than the connector level.
 
With AI Discovery, API Registry, LLM Registry, and Skill Registry, Cequence governs the MCP, API, and LLM channels an agent uses under one identity and one policy system — regardless of whether that agent runs on Claude, ChatGPT, or Copilot.
 
None of the three platforms governs that on its own. Cequence AI Gateway does.