Governance · Compliance · Security

The control plane for enterprise AI agents.

Governance for every AI agent.

AI agents now move money, write code, and call APIs inside your enterprise. Helixar is the control plane that lets you deploy them safely.

Recognised across the ecosystem

Helixar protocols, tools, and the team are referenced and supported across these programmes and partners.

  • Google
  • NVIDIA
  • Hugging Face
  • SPICE Protocol
  • Rilla Network

Why now

Boards, regulators, and CISOs are asking these every week.

Endpoint, identity, DLP, and application security each do their job, none of them was built to answer these on its own. Helixar exists to answer them together.

  1. 01

    Which AI agents are operating inside our environment right now?

  2. 02

    What are they authorised to do, and who authorised them?

  3. 03

    What did they actually do, and can we prove it?

  4. 04

    If one of them goes wrong, how do we stop it before it causes harm?

The coverage gap

The existing stack wasn’t built for this.

Each layer of your security stack does its job well. None of them was designed for an entity that plans, adapts, persists, and acts across systems faster than any human reviewer can keep up with.

Layer · 01

Endpoint Security

Built for

Malware on user laptops and servers.

Can’t answer

What an authorised AI agent did with valid credentials in the last hour.

Layer · 02

Identity & Access

Built for

Humans logging in from devices with passwords and MFA.

Can’t answer

A non-human entity that plans, adapts, and persists across sessions.

Layer · 03

Data Loss Prevention

Built for

Files at rest and email in motion at well-known choke points.

Can’t answer

An agent reading from a sanctioned API and acting on what it learns.

Layer · 04

Application Security

Built for

Code that ships through review, static analysis, and dependency scanning.

Can’t answer

Code that decides, at runtime, on behalf of someone, against tools you authorised.

Helixar treats the AI agent as the protected entity, not the user, the file, or the endpoint.

That changes what we measure, how we correlate, and where we intervene.

The control plane

Three things that, together, make autonomous AI deployable inside the enterprise.

Governance gives you the inventory. Compliance gives you the evidence. Security gives you the intervention.

01one register

Governance

Know every agent operating in your environment.

A single, authoritative view of every AI agent, what it is, who owns it, what it is permitted to touch, and what policies apply to it. Agents are first-class entities with their own identity, lineage, and authorisation chain.

  • Agent inventory across vendor SaaS, internal platforms, and personal connectors
  • Identity, ownership, scope, and policy in one record
  • Lineage that survives model swaps and tool changes
02four frameworks

Compliance

Tamper-evident evidence auditors actually accept.

Every agent action, every tool call, data access, and escalation, captured in a record that maps cleanly to control objectives. Auditors and regulators see that controls operated, not just that controls existed.

  • EU AI Act, NIST AI RMF, ISO/IEC 42001, SOC 2
  • Per-action evidence mapped to control objectives
  • Tamper-evident records via the HDP delegation chain
03four modes

Security

Graduated runtime enforcement, not just telemetry.

Monitor agent behaviour at runtime and intervene when it crosses from authorised activity into harm. Pick the level per agent and per action class, observe, alert, require approval, or block, without writing rules for threats you have not seen yet.

  • Cross-layer correlation across API, tool, and data access sequences
  • Per-action-class enforcement: observe · alert · approve · block
  • Reversible by design, start in observe, turn enforcement on when ready

What design partners say

Live in production. Already part of their governance stack.

From design partners deploying agentic AI into customer-facing and operational workflows.

Already production infrastructure. Audit trails our auditors accept for SOC 2.

As we prepared for our first major enterprise integrations, securing our AI agents became a top priority. BearTrap has become our operational control plane for every interaction with LLM APIs, scoped and time-bound keys, hard budget caps, workload binding, and cryptographically signed audit trails that our auditors fully accept for SOC 2. Helixar 360 added another layer by spotting subtle behavioral patterns in a Claude Code session that no rules-based tool would have caught. It’s reassuring to have this level of visibility. We’re already treating BearTrap as production infrastructure and look forward to bringing the full Helixar platform into our governance stack upon general release.

Pul Bandara

CTO, Rilla Network

Rilla Network

Clear, actionable insight into agent behaviour. Without writing a single rule.

Using Helixar over the past month while heavily relying on Claude Code for development has been reassuring. With broad permissions across tools and data, it’s easy to see how quickly things could go wrong. Helixar’s monitoring gave us clear, actionable insights into agent behavior without any pre-configured rules. The LLM Key management feature is excellent, especially for team-wide usage across our AI workflows and messaging bots. We’re a small team experimenting heavily, and this has already improved both our security posture and operational confidence. Looking forward to notifications and the upcoming features in the pipeline.

Pramodya De Alwis

Co-founder, SPICE

SPICE

If you’re accountable for AI risk, we’d like to talk.

CISOs, Chief Risk Officers, Heads of Compliance, Chief AI Officers, platform leaders, and boards asking harder questions than they used to.

Talk to the team