AI Agent Security
& Threat Mitigation
AI agents have a broader attack surface than traditional software. Prompt injection, data exfiltration, delegation loops, unauthorized access — JieGou's governance stack mitigates each vector with PII detection, Graduated Autonomy, cycle detection, and 30-type audit logging.
Attack Surface
The AI agent attack surface is broader than traditional software
Traditional software has known inputs and outputs. AI agents accept natural language, make autonomous decisions, delegate to other agents, and access external tools. Every capability is a potential attack vector.
Prompt Injection
Malicious input designed to override system prompts, bypass safety guidelines, or make the agent perform unintended actions.
Input sanitization, system prompt isolation, confidence scoring, and PII detection catch injection attempts before they reach the LLM. Graduated Autonomy ensures high-risk actions require human approval.
Data Exfiltration
Agents extracting sensitive data — PII, financial records, proprietary information — and sending it to unauthorized destinations via tool calls or output channels.
PII detection with reversible tokenization strips sensitive data before LLM processing. Envelope key encryption (BYOK) protects data at rest. Data sensitivity labels (coming) classify information by risk level.
Delegation Loops
Multi-agent systems where Agent A delegates to Agent B, which delegates back to Agent A — creating infinite loops that consume resources, generate costs, and produce no useful output.
Multi-agent cycle detection identifies and breaks delegation loops automatically. Delegation depth limits cap how many times agents can chain delegations. Shared memory isolation prevents agents from corrupting each other's state.
Unauthorized Access
Agents accessing tools, data sources, or performing actions beyond their authorized scope — either through misconfiguration or exploitation of overly broad permissions.
RBAC with 5 roles and 20 granular permissions. Graduated Autonomy constrains what agents can do without approval. MCP server permission scoping enforces least-privilege access. Complete audit logging of 30 action types provides forensic evidence.
Security Stack
7 layers of AI agent security
JieGou's security isn't bolted on — it's woven into every layer of the platform. From PII detection at the input to audit logging at the output, every agent action passes through multiple security checkpoints.
PII Detection & Reversible Tokenization
Automatically detect personally identifiable information — names, emails, phone numbers, addresses, SSNs, credit card numbers — before data reaches the LLM. Sensitive data is replaced with reversible tokens, so outputs can be de-tokenized for authorized users while the LLM never sees the raw data.
Graduated Autonomy — 4 Trust Levels
Four autonomy levels from Full Manual to Fully Autonomous. Agents start supervised and earn independence through consistent performance. Trust escalation is policy-driven with automatic demotion on accuracy drops. Different policies per department, per workflow.
Multi-Agent Cycle Detection
Real-time detection of delegation cycles in multi-agent workflows. When Agent A delegates to Agent B delegates to Agent A, the cycle is detected and broken automatically — before resources are wasted or costs spiral.
Delegation Depth Limits
Configurable caps on how many times agents can chain delegations. Prevents both intentional abuse and accidental infinite delegation chains in complex multi-agent orchestrations.
Audit Logging — 30 Action Types
Every agent action is logged with 30 distinct action types — tool invocations, LLM calls, delegation events, approval decisions, data access, configuration changes, and more. Full forensic trail for compliance, incident response, and governance.
Envelope Key Encryption (BYOK)
All API keys, credentials, and sensitive configuration are encrypted with AES-256-GCM using envelope encryption. Bring Your Own Key (BYOK) support lets enterprises manage their own encryption keys — JieGou never has access to your raw credentials.
Data Sensitivity Labels
Classify data by sensitivity level — Public, Internal, Confidential, Restricted. Sensitivity labels flow through the entire pipeline: which agents can access what data, which outputs require approval, which results can be shared externally.
Pipeline
Every agent action passes through the security pipeline
Comparison
AI agent security compared
| Capability | JieGou | Others |
|---|---|---|
| PII detection | Built-in, real-time, reversible tokenization | Most: None | Microsoft: DLP add-on |
| Autonomy levels | 4 levels with trust escalation | All: Binary HITL or none |
| Cycle detection | Automatic, real-time | All: None built-in |
| Delegation limits | Configurable depth caps | All: None built-in |
| Audit trail | 30 action types, full forensic trail | Zapier: Basic | LangSmith: Traces | n8n: Logs |
| Key encryption | AES-256-GCM envelope encryption, BYOK | Varies — most use platform-managed keys |
| Data classification | Sensitivity labels (coming) | Microsoft: Purview | Others: None |
Your AI agents need security that matches their capabilities.
PII detection, Graduated Autonomy, cycle detection, audit logging, and BYOK encryption — built into every JieGou plan. Start free with full security from day one.