Defender Impact
One of the most consequential gaps in enterprise AI security today is the lack of real-time visibility into what AI agents actually do at runtime — not what they are configured to do, but what actions they take, in what sequence, and against what resources. Kontext Security’s emergence closes that gap with a platform purpose-built for runtime enforcement of AI agent behaviour.
Capability Overview
Kontext Security has launched from stealth with $4 million in funding and a runtime enforcement platform designed to evaluate AI agent actions in real time. Rather than relying solely on pre-deployment policy definitions or post-hoc log review, the platform operates at the execution layer — observing agent behaviour as it unfolds and applying controls in-flight.
The core proposition is visibility and control: defenders gain a live view of what agents are doing, and the platform enforces guardrails that can intervene before a harmful or policy-violating action completes. This positions Kontext in the emerging category of AI agent governance infrastructure, sitting between the agent orchestration layer and the downstream tools, APIs, and data stores that agents interact with.
At $4 million in funding, Kontext is early-stage, and the article does not detail which agent frameworks are supported, the enforcement mechanism architecture, or how policy definitions are structured. However, the framing — runtime evaluation, visibility, control — maps directly to the defensive requirements that security teams have been articulating as agentic deployments accelerate.
Defensive Advances
In-flight behavioural enforcement. For the first time, defenders gain a mechanism to interrupt agent actions that deviate from expected behaviour before those actions complete. This is a meaningful advance over static access controls, which constrain what an agent can do but cannot respond dynamically to what it is doing.
Operational visibility into agent execution. Security and platform teams can now observe agent action sequences in real time, creating an audit-capable record of agent decisions and tool invocations. This supports both incident response and compliance workflows that have been largely blind to agentic activity.
Reduced blast radius from excessive agency. By enforcing controls at runtime, the platform limits the downstream damage when an agent is manipulated, misconfigured, or behaves unexpectedly — directly addressing the OWASP LLM08 Excessive Agency risk category.
Residual Gaps
Several maturity questions remain before organisations can fully realise the benefit of this capability.
Integration coverage is unknown. The article does not specify which agent frameworks, orchestration platforms, or cloud environments Kontext currently supports. Organisations running heterogeneous agent stacks will need to validate compatibility before committing to the platform.
Policy definition maturity is a prerequisite. Runtime enforcement is only as good as the policies it enforces. Organisations that have not yet defined expected agent behaviour baselines — what tools an agent should invoke, in what order, against what data — will need to complete that groundwork before enforcement provides meaningful value.
Early-stage vendor risk. At $4 million in seed funding, Kontext carries the typical risks of an early-stage vendor: limited support capacity, evolving product surface, and uncertain roadmap. Security teams should apply appropriate vendor risk evaluation before integrating into production agent workflows.
Coverage of multi-agent pipelines. Whether the platform can enforce across chained or multi-agent architectures — where one agent delegates to another — is not addressed in available reporting. This is an important capability gap given the direction of enterprise agentic deployments.
Framework Mapping
Kontext’s runtime enforcement capability is most directly relevant to the following frameworks:
- AML.T0086 / AML.T0098 — Exfiltration and credential harvesting via agent tool invocation: runtime monitoring can detect anomalous tool calls consistent with these techniques.
- AML.T0080 — AI Agent Context Poisoning: behavioural deviation detection may surface the downstream effects of context manipulation.
- LLM08 - Excessive Agency: the platform directly operationalises controls against this category by enforcing action-level guardrails.
- LLM07 - Insecure Plugin Design: runtime enforcement provides a compensating control where plugin-level security is insufficient.
Deployment Considerations
Organisations should approach Kontext deployment in a sequenced manner. Begin with agent inventory and behaviour baseline documentation — runtime enforcement cannot be calibrated without understanding what normal looks like. Pilot in a non-production environment to validate integration compatibility and policy logic before exposing enforcement to live agent workflows. Treat the platform as a complementary control alongside identity-based access restrictions and agent sandboxing, not a replacement for them.
Defender Checklist
- Complete an inventory of all deployed AI agents, their tool permissions, and expected action sequences
- Define baseline behavioural policies for each agent class before enabling enforcement
- Validate Kontext’s integration compatibility with your agent frameworks and orchestration stack
- Assess vendor risk using standard third-party evaluation criteria appropriate for early-stage suppliers
- Pilot in a non-production environment and tune alerting thresholds before production rollout
- Integrate runtime alerts into existing SIEM/SOAR workflows to maintain unified incident response