INDUSTRY FIRST

AI Agent Security Suite

Secure AI agents before they execute. Validate every tool call. Enforce policies. Complete audit trails.

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pip install safekeylab

from safekeylab.agent_security import SecureAgent

# Wrap any agent with security
secure_agent = SecureAgent(your_agent, policy=my_policy)
result = secure_agent.run("Process this request")

Why AI Agents Need Security

⚠️

Unrestricted Tool Access

AI agents can execute any tool they're given access to. A compromised prompt could trigger destructive actions.

🔓

No Audit Trail

Most agent frameworks don't log what tools were called or why. When something goes wrong, you can't investigate.

💣

Injection Vulnerabilities

Malicious inputs can manipulate agents into executing unintended commands, accessing restricted data, or exfiltrating information.

📈

Runaway Costs

Without rate limiting, agents can make unlimited API calls, running up massive bills or overwhelming downstream services.

Agent Security Features

CORE

Tool Call Validation

Every tool call is validated before execution. Check for:

  • Command injection patterns
  • Path traversal attempts
  • SQL injection in arguments
  • PII in tool parameters
  • Unauthorized domain access
# Validate before execution
result = validator.validate_tool_call(
    tool_name="web_search",
    arguments={"query": user_input},
    context=agent_context
)

if not result.allowed:
    print(f"Blocked: {result.reason}")
    # Severity: {result.severity}
POLICY

Policy Engine

Define exactly what your agents can and cannot do:

  • Allowlist specific tools
  • Block dangerous operations
  • Restrict domain access
  • Set rate limits per tool
  • Require human approval for sensitive actions
# Define agent policy
policy = AgentPolicy(
    agent_id="customer-support-bot",
    allowed_tools=["search", "lookup_order"],
    blocked_tools=["delete", "execute_sql"],
    allowed_domains=["api.internal.com"],
    max_tool_calls_per_minute=30,
    require_human_approval_for=["refund"]
)
AUDIT

Complete Audit Trail

Know exactly what your agents did and why:

  • Every action logged with timestamp
  • Full input/output capture
  • Policy decisions recorded
  • Session replay capability
  • Anomaly detection
# Replay entire session
trace = audit_trail.get_session_trace(session_id)

for action in trace:
    print(f"{action.timestamp}: {action.tool_name}")
    print(f"  Status: {action.status}")
    print(f"  Policy: {action.policy_decision}")

# Detect anomalies
anomalies = audit_trail.detect_anomalies(
    agent_id="my-agent",
    window_hours=24
)

Works With Your Stack

LangChain

from safekeylab import SafeKeyLabCallback

agent = create_react_agent(
    llm, tools, prompt,
    callbacks=[SafeKeyLabCallback()]
)

OpenAI Assistants

from safekeylab import SecureAgent

secure = SecureAgent(
    openai_assistant,
    policy=my_policy
)

Custom Agents

from safekeylab.agent_security import (
    ToolCallValidator,
    PolicyEngine
)

# Validate any tool call
validator.validate_tool_call(...)

Why SafeKeyLab for Agent Security?

Feature SafeKeyLab DIY / Open Source Other Vendors
Tool Call Validation ✅ Built-in ❌ Build yourself ❌ Not available
Policy Engine ✅ YAML/JSON policies ❌ Build yourself ❌ Not available
Audit Trail ✅ Complete logging ⚠️ Basic logging ❌ Not available
Anomaly Detection ✅ ML-based ❌ Build yourself ❌ Not available
Integration Time ✅ 5 minutes ⚠️ Weeks N/A

Secure Your AI Agents Today

No other platform offers comprehensive agent security. Be first to protect your AI.

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pip install safekeylab • No credit card required