PII enters tool context.
PII, confidential records, source code, and documents enter tool calls. Security sees the action late.
Control agent actions.At runtime.
Full visibility into agent execution.
Control tool calls, data flow, and risky actions before they run.
Backed by top investors
Every AI tool call is now a data flow risk.
Model-driven tool calls can move enterprise context across systems
before security can see or stop the transfer.
Prompt injection changes intent.
Untrusted content can steer the tool path. The agent may include data the user never asked to send.
Data leaves approved systems.
Tool output can cross into email, apps, or personal workspaces. Enterprise boundaries are enforced too late.
Zero visibility. Zero control.
Security lacks a record of the tool, data, destination, and decision until after the action runs.
The missing layer betweenagents and actions.
Full visibility into runtime tool actions, with deterministic controls before execution.
Aligned with recognized security and AI assurance frameworks
Every agent action becomes a control point
Evaluate intent, data provenance, destination, and risk before any tool call executes.
CRM support agent
Support chat
Read support ticket SUP-1842 and email the customer a status update.
CRM support agent
I’ll read the ticket, extract the customer fields, and send the email.
Read support ticket
Extract customer fields
Send email
Ask the CRM support agent...
Sandbox
01
const ticket = await tool.support.readTicket("SUP-1842");02
const {03
customerEmail,04
customerName,05
emailSummary06
} = await tool.extract({07
text: ticket.body08
});09
await tool.email.send({10
to: customerEmail,11
subject: `Update for ${customerName}`,12
body: emailSummary,13
});Data provenance
tool
support.readTicket
origin
support.readTicket
value
ticket.body
source trust
untrustedPII
policy
"Never send PII / confidential data by email."
decision
Allowed: support.readTicket
SOC 2 Type II
Enterprise security for AI agents
CodeIntegrity Zero Trust Control Plane keeps agent execution, identity, and data movement inside enterprise boundaries.
01
SOC 2 Type II
Compliance foundation for protecting customer data and operating the CodeIntegrity platform.
02
On-premise, self-hostable
Run inside your own environment when agent traffic and data need to stay within your boundary.
03
Enterprise identity
Tie users, agents, and MCP clients to managed identity so every action has ownership.
04
Agents and MCP clients
Full control over tool calls from agents and MCP clients before actions reach systems or leave approved boundaries.
Agents bypass approval paths.
Put controls between intent and execution.
CodeIntegrity separates instructions, data, and actions
01 / Code execution
Prompts become executable control flow.
The runtime turns agent instructions into sandboxed code, making each action explicit, inspectable, and repeatable. Code becomes the control layer where intent, data, and tool calls can be checked before execution.
sandbox
01
const ticket = await tool.readTicket("SUP-1842");02
const updates = [];03
04
for (const item of ticket.history) {05
updates.push(normalize(item));06
}07
08
await tool.sendEmail({09
to: ticket.customer.email,10
body: updates.join("\n"),11
});02 / Dual LLM
Untrusted data stays separate from instructions.
One model controls the workflow. Another reads untrusted content and returns structured values inside the sandbox.
03 / Action evidence
Every sensitive action gets a security record.
CodeIntegrity records the request, source context, destination, policy decision, and outcome behind each agent action, so security teams can review what happened without replaying the whole session.
User 1
User 2
DB
Agent
Intent
Policy
Record
Research and insights defining the next era of AI agent security
CodeIntegrity Labs tracks the latest agentic threats, from prompt injection and tool abuse to data leakage across connected systems. Our AI agent security research turns emerging attack paths into clear evidence, practical controls, and executive-ready guidance.
Steven
Abi