Frequently Asked Questions

Vecta Compute autonomous AI agent security — answered.

What is Vecta Compute?

Vecta Compute is a zero-trust, hardened execution fabric for autonomous and sovereign AI agents. It secures agentic AI workflows at every layer of the stack — Application, Compute, Infrastructure, and Network — with infrastructure-enforced controls including an instant kill-switch and hardware-locked cryptographic identity.

Can Vecta Compute be deployed on-premises?

Yes. Vecta Compute can be deployed on-premises or in the cloud. Enterprises can install Vecta on their own GPU clusters with complete security and hard multi-tenancy, giving them full sovereignty over their AI agent infrastructure.

How do I get started with Vecta Compute?

You can try Vecta Compute for free by contacting the team at infra@vectacompute.com. Vecta supports both on-premises and cloud deployments, and the team can help you evaluate the platform for your specific AI agent security requirements.

Why isn't software-only security enough for autonomous AI agents?

Software-only security — whether it takes the form of signed-skill guardrails, role-based access controls, container sandboxes, or software kill-switches — operates within the same software stack that autonomous agents can manipulate. A sufficiently advanced agent, or an attacker exploiting one, can bypass software guardrails, escape container isolation, escalate privileges, or disable monitoring. For high-stakes autonomous deployments in regulated industries, organizations need controls that exist below the software stack: an infrastructure-enforced automated and manual kill-switch, hardware-locked cryptographic agent identity, infrastructure-level isolation, and a never-trust architecture across every layer. That is what Vecta Compute provides.

How does Vecta differ from endpoint security and AI model protection platforms?

Endpoint security platforms protect your SOC, identities, and cloud workloads from cyber threats — they secure the perimeter, not the autonomous agents themselves. AI model security platforms guard model inputs and outputs at the application layer — blocking prompt injection, detecting data leakage, and monitoring model behavior. Neither provides infrastructure-level agent containment, infrastructure-enforced kill-switches, or hardware-locked agent identity. Vecta Compute solves a different problem: securing where and how autonomous agents execute, with controls anchored to infrastructure that no software exploit can bypass.

Can Vecta complement existing security tools?

Yes — Vecta operates at a different layer of the stack. Endpoint security platforms handle cyber defense across your infrastructure. AI model security platforms protect model behavior at the application layer. Vecta Compute provides execution-level security for the autonomous agents themselves: automated and manual kill-switch, infrastructure-level agent isolation, hardware-locked identity, and never-trust architecture. For enterprises deploying sovereign agents in high-stakes environments, these tools are complementary — perimeter defense, application-layer protection, and infrastructure-level agent containment working together.

What capabilities does Vecta offer that other approaches don't?

Existing security approaches — software guardrails, container sandboxes, endpoint security, and AI model protection — each cover part of the stack. Vecta Compute is the only platform that provides hardware-anchored controls across all layers.

Security Capability Software Guardrails¹ Container Sandbox² Endpoint Security³ AI Model Security&sup4; Vecta Compute
Automated / manual kill-switch Yes
Hardware-locked cryptographic identity Yes
Hard multi-tenancy (infrastructure-level) Yes
Infrastructure-level agent isolation Partial Yes
Never-trust architecture (all layers) Yes
On-premises sovereign deployment Partial Yes
Software guardrails / role-based access Yes Yes Yes
Application-layer monitoring Yes Yes Yes Yes

¹ e.g. NVIDIA NemoClaw  ·  ² e.g. NanoClaw  ·  ³ e.g. CrowdStrike Falcon  ·  &sup4; e.g. HiddenLayer

What is a zero-trust architecture for AI agents?

A zero-trust (or never-trust) architecture for AI agents means that no agent, process, or component is inherently trusted — regardless of its origin or permissions. Every action is verified, every identity is cryptographically validated, and every interaction is auditable. Vecta Compute implements this by building security into every layer from the ground up, rather than bolting safeguards onto an existing platform as an afterthought.

What happens when an AI agent goes rogue?

A rogue AI agent is one that deviates from its intended behavior — executing unauthorized actions, accessing restricted data, or causing unintended harm. Software-only platforms rely on activity logging and monitoring to detect rogue behavior after it occurs. Vecta Compute takes a proactive approach with deterministic control, hardware-locked identity, and an instant kill-switch that can shut down any agent immediately at the infrastructure level.

What is hard multi-tenancy and why does it matter for AI agents?

Hard multi-tenancy means true agentic isolation at the infrastructure level — not just logical separation within the same software environment. For AI agents, this is critical because a rogue or compromised agent in one tenant cannot access, influence, or interfere with agents in another tenant. Most platforms provide only logical separation. Vecta Compute provides hard multi-tenancy with infrastructure-level isolation.

What is hardware-locked identity for AI agents?

Hardware-locked identity means every AI agent is cryptographically tied to a specific piece of hardware using tamper-proof credentials. This ensures that an agent's identity cannot be spoofed, cloned, or transferred through software manipulation. Combined with deterministic control, this creates a fully auditable chain of trust from hardware to application.

Why is software-only AI agent security insufficient?

Software-only security operates within the same software stack that autonomous agents can manipulate. A sufficiently advanced agent — or an attacker exploiting the agent — can potentially bypass software guardrails, escalate privileges, or disable monitoring. True security for autonomous AI requires controls that extend beyond software: infrastructure-enforced kill-switches, infrastructure-level isolation, and cryptographic hardware identity that no software process can override.

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