kaizen-security 0.1.0

Pluggable enforcement for AI agent actions. Inspect every tool call and block known-bad.

kaizen-security

Pluggable enforcement for AI agent actions. Inspect every tool call, skill load, or outbound connection, and block the known-bad before it reaches your data. Zero runtime dependencies.

Install

pip install kaizen-security

Quickstart

from kaizen_security import Kaizen

kz = Kaizen(api_key="kz_live_...")          # syncs policy from the control plane

verdict = kz.inspect(tool="clawhub2", publisher="hightower6eu", target="91.92.242.30")
if verdict.blocked:
    print(verdict.reason)                    # blocked by policy: blacklisted publisher, ...
    for f in verdict.evidence:
        print(f.kind, f.value)

Raise on a block instead of branching:

from kaizen_security import KaizenBlocked

try:
    kz.enforce(tool="clawhub2", publisher="hightower6eu")
except KaizenBlocked as e:
    handle(e.verdict)

Wrap a tool function:

@kz.guard
def call_tool(name, **kwargs):
    ...

Run it fully local, no account

from kaizen_security import Kaizen, Policy

policy = Policy(mode="blocklist", rules={
    "publishers": ["hightower6eu"],
    "ips": ["91.92.242.30"],
    "skill_patterns": [r"^clawhub[0-9]*$"],
})
kz = Kaizen(policies=[policy], report=False)

The contract

inspect(action) -> Verdict(decision, reason, evidence) where decision is allow or block. Enforcement runs locally for low latency. When an api_key is set, the client syncs policy from the control plane and reports verdicts back for the dashboard, fire and forget so it never adds latency.

Modes

• blocklist: block on a match against blacklisted publishers, IPs, domains, skill patterns, or hashes.
• allowlist: allow only approved publishers or tools, block the rest.
• correlation: flag a risky session sequence, for example a sensitive read followed by an outbound connect.