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3 min readPerspectives & Ideas

Decision Keep vs model monitoring: what's the difference?

Observability tells you how a system behaves now; a Forensic Witness record proves what a specific automated decision was. Here is how Decision Keep and your monitoring stack work together.

About the author+

Jamil Luketic

Executive Director at Decision Keep

Former Data & Tech Leader at Oracle, Mastercard, Coles, Optus, and Reece.

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Illustration for Decision Keep vs model monitoring: what's the difference?
Perspectives & Ideas

A common first reaction: "We already have monitoring. Why do we need this?"

It is a fair question, and the answer is that the two solve different problems. McKinsey, Gartner and PwC all frame observability as an operational discipline - invaluable, but distinct from evidentiary proof. Conflating them is one reason AI governance stalls.

Monitoring = how the system behaves

Your monitoring stack (datadog, grafana, a model registry, drift detectors) answers: is the system healthy right now? It tracks latency, throughput, error rates, feature drift, and maybe prediction distributions. That is operational intelligence - invaluable, and not what we replace.

Decision Keep = what each automated decision was

Decision Keep answers a different, juridical question: for this specific automated decision, what was decided, by which model version, when, and can we prove the record is intact? Every automated decision is signed with your key, hash-chained, time-anchored, and verifiable offline.

Side by side

Model monitoring Decision Keep
Question How is the system behaving? What did this automated decision do?
Time focus Now / trends The moment of each decision
Integrity Rarely signed Signed + hash-chained
Verifiability Vendor console Offline, against your key
Audience Engineers / SREs Auditors / regulators / board
Evidence type Operational Juridical

They work together

The strongest setups use both. Monitoring catches a model degrading in real time; Decision Keep preserves the evidentiary record of every automated decision that model made, so when someone asks what happened on a specific day, the answer is provable - not reconstructed from metrics.

A credit team at a regional bank had Datadog dashboards and Grafana alerts healthy on the day a customer disputed a loan decision from six months prior. Ops could show the model was performing within bounds. They could not show what the model actually decided for that specific application. The regulator asked for the record. The bank had a story, not evidence.

Monitoring tells you the engine is running. Decision Keep is the flight recorder.

For GRC teams building controls, see Audit-ready AI: how GRC teams prove every automated decision.

How to start

Keep your monitoring. Add Decision Keep beside it to produce the independent, verifiable Forensic Witness for every automated decision.

FAQ

Questions auditors, risk and legal actually ask

Is Decision Keep a monitoring tool?+
No. Monitoring tells you how a system is behaving now - latency, drift, error rates. Decision Keep proves what a specific automated decision was, when, by which model version, and whether the record is intact. They are complementary: monitoring is operational, the Forensic Witness is juridical.
Do I need to replace my observability stack?+
No. Decision Keep sits beside your existing monitoring and logging. It adds the tamper-evident, verifiable decision record that observability tools were never designed to provide.
Can monitoring replace an audit trail?+
Not on its own. Metrics dashboards are usually editable and rarely signed, so they prove what someone claims happened, not what actually happened. An auditor needs integrity guarantees a monitor does not give – a Forensic Witness provides that.

Sources

References & further reading

Independent analysis and standards cited in this article.

Prove every AI decision

Decision Keep gives your organisation a tamper-evident, verifiable record of every automated decision. Book a demo to see it on your stack.

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Documentation

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