Zylon AI — agentic threat model
Zylon AI presents a moderate agentic risk profile, mitigated significantly by its on-premises deployment and data governance focus, though its proactive task execution and access to sensitive corporate data require robust local infrastructure security.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.60 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Integrates with open-source AI models. Risks include model alignment issues, adversarial prompt injection, and potential model-level vulnerabilities inherent to the selected open-source LLMs.
Not certain from the listing — details on vector databases or RAG pipelines are not specified, but data governance tools are mentioned, suggesting some level of structured data management and access control.
Not certain from the listing — the specific orchestration framework is not disclosed, but the platform supports proactive task execution and customizable workflows, which could introduce risks of tool misuse or insecure execution paths.
100% private deployment (on-premises or private cloud) significantly reduces external exposure but shifts the burden of infrastructure security, sandboxing, and network isolation entirely to the customer.
Not certain from the listing — specific evaluation, guardrails, or observability stacks are not detailed, though data governance and compliance tools are present to assist with monitoring.
Strong focus on compliance and data governance for private deployments, though specific identity/access management controls and audit logging mechanisms are not detailed.
Not certain from the listing — collaboration features are mentioned, but it is unclear if this involves multi-agent orchestration, agent-to-agent trust boundaries, or marketplace integrations.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).