Secure Custom AI Agents

Secure Custom Agentic Workflows

Custom AI agents are built to automate an organization’s unique workflows, connect to internal systems, and take action using enterprise data. These agents can accelerate productivity and enable powerful automation across a wide range of development approaches. Agents built on platforms such as Vertex AI, Azure AI Foundry, and Amazon Bedrock, developed using LangGraph or LangChain with python and other proprietary architectures introduce security challenges.

Because custom agents combine model reasoning, enterprise data access, and autonomous action, they create a rapidly expanding attack surface that traditional application security tools were never designed to protect.

Custom Agent Workflow

01

Employee/Customer

A user initiates a financial analysis or question

02

Custom UI or Internal App

User interacts through a custom interface or internal application

03

Custom Agent Logic

Handles context, session management, input validation, and business rules

04

LLM (GPT, Gemini, Claude, Llama)

Generates insights, answers, and analysis based on user intent and data

05

Planner/Orchestrator

Breaks down the task, plans steps, selects tools, and orchestrates execution

Enterprise Access. Enterprise Risk

Custom agents are AI systems built by internal teams or trusted partners, whether self-managed orchestration frameworks or managed agent platforms. To complete AI-driven enterprise workflows, custom agents often integrate with:

Orchestration Frameworks
LLM APIs
MCP Servers
RAG Pipelines
Internal Tools and Applications
Business Logic

These agents frequently operate with privileged access, making visibility, governance, and runtime protection across the AI lifecycle essential.

Custom agent integration hub connecting LLM APIs, orchestration, MCP servers, and RAG pipelines around a central node

The Security Risks of Custom Agents

Custom agents can connect directly to internal APIs, databases, and workflows, turning a single prompt into a potential business problem. Custom agents introduce the following risks:

Prompt injection and tool misuse that manipulate the agent into taking unintended action

Over-permissioned access to internal systems, APIs, files, and workflows

Data leakage through model outputs, tool calls, logs, or connected services

Unsafe autonomous behavior when agents make decisions without enough policy control

Shadow AI development where agents are built and deployed without security visibility

How TrojAI Secures Custom Agents

TrojAI helps enterprises secure custom agents across the full AI lifecycle. From design and configuration to runtime monitoring, policy enforcement, and continuous protection, TrojAI helps secure custom agents across the full AI lifecycle. Security and AI teams get the control layer they need to safely build and scale custom agents.

Discover and Assess Agent Risk

Identify where custom agents exist, what systems they connect to, and which tools they can access.

Test Before Deployment

Evaluate agents before and during deployment for susceptibility to prompt injection, jailbreaks, unsafe tool use, data exposure, and policy violations before they reach production.