Responsible AI
Name the risk before you claim to have mitigated it.
NeuralSeek names AI risk from a taxonomy it did not write — the independently published AI Risk Atlas — so the gaps stay visible instead of being edited out.
The lifecycle
Five places risk originates.
Risk is not one category, and a single filter at the end of the pipeline does not touch most of it. The atlas separates risk by the stage it comes from — which is also where a control has to sit to do anything about it.
From risk to control
What actually runs.
Each of these is enforced when a request arrives or an answer leaves, not documented in a policy that nobody reads at runtime.
Checked as the request arrives.
The same primitives, every deployment.
They are the same primitives every deployment ships with — there is no responsible-AI tier.
Grounded, scored, cited
Answers are built from your own corpus, scored for confidence, and traceable back to the passage they came from — so an accuracy claim has something to be checked against rather than being taken on trust.
Policy enforced at runtime
Guardrails apply to the request as well as the response, so a risk that arrives at the input boundary is handled where it arrives instead of being left to the wording of a prompt.
Lineage you can replay
Reconstruct any run — which sources, which model, which policy, which version. That is what turns a governance risk from an argument into an answerable question.
Or close the data plane entirely
Where the underlying concern is data egress or third-party visibility, running the containers inside your own perimeter — up to fully air-gapped — removes the exposure rather than mitigating it.
PII detection and redaction
13 detector categories and five enforcement actions — mask, flag, hide, delete, or pass through with a warning — configurable per intent.
A forensic audit trail
Every question, answer, score and configuration change logged, timestamped, attributable, and exportable to S3, Splunk or Datadog.
Three questions a risk committee asks.
None of them is answered by a responsible-AI statement. All three are answerable by saying where the taxonomy came from and what runs when a request arrives.
Whose list of risks is this?
Not ours. It is an externally published taxonomy that consolidates AI risks from multiple sources, and it names risks NeuralSeek has no special answer to alongside the ones it does. That is the point of using someone else's list: the gaps stay visible instead of being edited out.
Is agentic AI a different problem?
Partly. The taxonomy tags each risk by whether it pre-dates generative AI, was amplified by it, or is specific to generative or agentic systems. The agentic tier is the one with no clean classical-ML analogue — tool use, function calling and multi-step autonomy create failure modes that did not exist when a model only produced text and a human decided what to do with it.
How does a named risk become an enforced control?
By mapping it to something that actually executes. An accuracy risk maps to grounding, scoring and citation; a privacy risk maps to detection and redaction before the data moves; a governance risk maps to the audit trail. Where a named risk maps to nothing that runs at request or response time, it is a policy document, not a control — and it should be described as one.
See it running inside your own boundary.
Talk to a NeuralSeek expert about how it fits your stack, where your data has to live, and the governance your auditors already expect.