The landscape
What none of the strong vendors below combines is a single layer that enforces a locally authored policy at the point of irreversible action and proves it, deployable in software today, with a hardware-rooted tier we grade openly as later rather than imply it ships. Below is our honest account of where each vendor is strong, where we differ, and where they are ahead of us today.
What this comparison is about
If you run AI governance tooling today, you likely run observability, a runtime guardrail, or a governance-workflow suite, and the strong ones are genuinely good at what they do: they measure behaviour, filter bad output, or document the process. Keep them. None of that is what this page compares. There is a fourth job none of them was built for: enforcing a locally-authored policy at the point of irreversible action, and emitting proof an outside party can replay. This page places the serious vendors honestly on that one axis, including where they are ahead of us.
Most AI-governance tooling sits in one of three places: observability (measure and flag model behaviour after the fact), runtime guardrails (filter or block individual inputs and outputs inline), or lifecycle governance (inventory, documentation, workflow and reporting across an AI estate). Our claim is narrower and deeper than any of these: a single non-bypassable enforcement-and-proof layer at the moment an action becomes irreversible, emitting tamper-evident proof against a locally declared policy. The nearest neighbour to that claim is hardware-attested verifiable compute, where one vendor, EQTY Lab, already ships.
What they do
Hardware-rooted verifiable compute on Intel and NVIDIA trusted execution environments, generating cryptographic certificates for AI operations, anchored to an immutable ledger.
What they do well
This ships today. Hardware-attested proof running on NVIDIA Confidential Computing, named collaborators, and real silicon-level attestation, the thing we describe as our destination.
Where we differ
EQTY notarises that a computation happened as recorded. Our claim is enforcement of a declared policy at the point of action: we physically refuse to actuate an out-of-envelope action, not only certify what occurred, and we author constraints from the organisation's own frameworks via PACE.
Where they're ahead of us
Their hardware attestation is in production; our Semantic Enforcement Appliance is later, post-validation. On hardware-rooted proof shipping now, EQTY leads.
What they do
ML observability and model monitoring, performance, drift, bias, explainability, and LLM/guardrail monitoring across the model lifecycle.
What they do well
Mature, widely deployed runtime observability with low-latency monitoring and strong dashboards; a proven answer to "is this model behaving and drifting."
Where we differ
Observability detects and reports; it does not make a prohibited action impossible, nor emit a tamper-evident proof an outside party can replay against a declared policy. Our aggregate-drift service overlaps with the detection half, then adds enforcement and per-action provenance on top.
Where they're ahead of us
Breadth and maturity of monitoring, and an established production install base across enterprises, traction we do not yet have in-domain.
What they do
Runtime AI security, algorithmic red-teaming and an inline "AI firewall" that inspects and blocks malicious or policy-violating inputs and outputs, inside Cisco's security portfolio.
What they do well
Strong on adversarial robustness and threat detection, backed by Cisco's distribution and security integration; a credible answer to prompt-injection and jailbreak-style attacks at runtime.
Where we differ
An AI firewall is a probabilistic filter in the software path, the same layer an untrusted model can influence. Our enforcement is designed to sit beneath that layer and to prove each decision rather than only intercept it. "Did we block the bad input" versus "can we prove the action stayed in policy."
Where they're ahead of us
Shipping, enterprise-grade runtime security with major-vendor backing and scale; our non-bypassable enforcement is deterministic only in the Rules tier today, with the strongest form still on the roadmap.
What they do
End-to-end AI lifecycle governance, model inventory, risk and compliance workflow, documentation, and regulatory reporting mapped to frameworks like the EU AI Act and NIST, the "bank-grade" incumbent for model risk management.
What they do well
Comprehensive, enterprise-trusted governance workflow with deep model-risk-management and audit-reporting features, and the procurement comfort of an established vendor.
Where we differ
watsonx.governance governs the process and documentation around models; it records attestations authored by the operator. We produce evidence generated by the running system at the moment of action, proof rather than documentation, and sit beneath such a stack rather than replacing it. The two are complementary.
Where they're ahead of us
Certifications, references, breadth, and procurement credibility of a global vendor. We have one production engine in an unrelated domain (pinpole) and no third-party attestation yet.
Where we're honestly positioned
We are not the most mature option in this list, and on hardware-attested proof shipping today, EQTY Lab is ahead. But where the others measure, filter, or document, our destination is to make the prohibited action impossible and the permitted one provable, deployable in software now (Rules and aggregate tiers), with the hardware-rooted tier graded openly as later rather than implied as present. We say plainly which parts of that are true today.