Six cognitive layers
Every input runs through a composable pipeline from instincts through to reflexes, each layer building on the one before it.
Content safety arranged in layers that can short-circuit, roughly the way people react to danger. Instincts catch injection and exfiltration in under 10ms. Awareness spots PII, topic and intent. Boundaries are hard deny lists. Values apply toxicity and brand rules, and judgment scores grounding and compliance last, because it is the expensive one.
Instincts, awareness, boundaries, values, judgment and reflexes, composed into a profile that can be swapped per tenant or per agent.
Metrics, audit trails and tracing attach at every stage of the pipeline without the engine changing.
Sensitive values are identified, tokenised and stored under AES-256-GCM with reversible access control.
Safety as a pipeline with named stages, so what was checked and what passed are both answerable after the fact.
Every input runs through a composable pipeline from instincts through to reflexes, each layer building on the one before it.
Sensitive values are redacted from the flow and held under AES-256-GCM with reversible access control, so a downstream system only ever sees a token.
Every scan, policy evaluation and audit record carries its tenant, which makes cross-tenant access structurally impossible.
Metrics, audit trails and tracing attach at each stage of the pipeline without the engine code being touched.
Instincts, awareness, boundaries, values, judgment and reflexes compose into a reusable profile, switched per tenant, per agent or per environment.
Reports for the EU AI Act, NIST AI RMF and SOC2 are built from scan results, policy evaluations and audit trails that were already being recorded.
The cheapest check, run first.
Context the later layers need.
Hard limits that do not negotiate.
The expensive layer, reached only when the others allow it.
Shield is content safety for Go, arranged as six layers ordered by cost and precision, any of which may end evaluation early.
Short-circuiting is the design, not an optimisation. A prompt with an obvious injection string never reaches the grounding check, because no grounding score could contribute to that decision.
A flat pipeline that runs everything needs a combination rule, and combination rules destroy explanation. "Blocked at instinct: jailbreak classifier, confidence 0.94" is auditable, showable to a customer, and testable. "Composite risk 0.71" is none of those, and tuning it moves the score for every other input too.
A profile composes the six primitives into a reusable safety character, the counterpart to a Cortex persona. A bank's support bot and an internal engineering assistant get different profiles rather than different toggle combinations on one shared pipeline.
A message blocked at instinct is never scanned for PII, so its PII content is unknown. That is correct for blocking and wrong for analytics. Running the full chain in shadow mode on a sample is the current workaround, not an answer I am happy with.
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