Agentic AI Development Services — agentic threat model

Not certain from the listing — as a development service, the foundation models are chosen per-project. Potential threats include adversarial prompt injection, model poisoning, or alignment issues depending on whether open-source or proprietary LLMs are deployed.

Not certain from the listing — data operations, vector databases, and RAG pipelines are custom-built for clients. Risks include training/RAG data poisoning, unauthorized data access, and lack of data lineage controls in bespoke integrations.

Not certain from the listing — while the service builds goal-driven, autonomous agents with self-learning capabilities, the specific orchestration framework (e.g., LangChain, AutoGen, or proprietary) is custom-tailored, posing risks of insecure tool binding and memory poisoning.

Not certain from the listing — deployment environments (cloud, on-premise, sandboxed) are client-defined. Enterprise-grade claims suggest support for secure infrastructure, but actual risks of container escape or privilege escalation depend on the final deployment architecture.

Not certain from the listing — evaluation, guardrails, and observability tools are not detailed. Custom implementations must establish robust logging and drift detection to prevent silent failures in autonomous decision-making.

Not certain from the listing — although 'enterprise-grade security' is advertised, specific compliance certifications (like SOC2, ISO 27001) or identity/access management (IAM) controls are not detailed and must be verified per engagement.

Not certain from the listing — the service can build multi-agent systems, but the specific ecosystem, agent-to-agent trust boundaries, and marketplace integrations are determined during custom development.