AI agents are being deployed into production systems at companies of every size — automating workflows, processing data, and managing infrastructure autonomously, often without a human reviewing each action. This creates a security risk the industry has been slow to address: what happens when an agent does something harmful?
Runtime security refers to the controls, monitoring, and enforcement mechanisms that govern what an AI agent does while it is executing — not before, not after, but during the action.
| Approach | What it protects | What it misses |
|---|---|---|
| Prompt filtering | Malicious inputs to the model | What the agent does with legitimate access |
| IAM / access control | Which systems the agent can reach | How it uses that access |
| Runtime security | Every action the agent takes | Nothing — covers full execution |
Every action is intercepted and analyzed before it executes, creating a complete audit trail of action type, target resource, payload, and timing.
Each action receives a risk score based on how destructive the action type is, how sensitive the target resource is, how the payload compares to the agent baseline, and whether the action rate is anomalous.
Rules defined by security teams are evaluated against each action before it runs. Violating actions are blocked, paused, or allowed depending on configuration.
When a violation is detected the system responds automatically — alerting operators, pausing the agent, or reversing recent actions. Automatic rollback is critical because many agent actions are difficult to reverse manually at scale.
pip install vaultakfrom vaultak import Vaultak, KillSwitchMode
vt = Vaultak(
api_key="vtk_...",
allowed_resources=["/tmp/*", "/data/readonly/*"],
blocked_resources=["prod.*", "*.env"],
max_risk_score=0.7,
mode=KillSwitchMode.PAUSE
)
with vt.monitor("my-agent"):
agent.run()Runtime security is to AI agents what a seatbelt is to a car. You hope you never need it. But you would never drive without one.