AIAdvanced#AI Safety#Evaluation#Deployment

Inference

Definition

The stage where a deployed model processes an input and produces an output. An inference result can be a label, score, generated text, or structured draft; it does not verify its own input, predict a market reliably, or authorize an action.

Source reviewed

NIST: Adversarial Machine Learning Taxonomy opens in a new tab
Supports: NIST describes risks across model training, deployment, and application stages, including attacks that compromise integrity, availability, or privacy rather than treating model output as self-verifying.
NIST AI RMF: Generative AI Profile opens in a new tab
Supports: NIST explains that generated output can be confidently false or internally inconsistent, especially consequential when people act on it.
OWASP: LLM06 Excessive Agency opens in a new tab
Supports: OWASP documents damaging actions that can follow when applications give broad tools or authority to unexpected, ambiguous, or manipulated LLM output.

Concept Diagram

Key Takeaways

Inference runs a deployed model on an input and returns an output; it does not prove that the input or output is correct.

Record the model version, input source and time, preprocessing, prompt, tool calls, output, limitations, and later approval.

Low latency, a confidence score, or a model-generated explanation is not verification of a market claim or upstream data.

Keep signing, trading, transfers, and access changes behind independent policies and explicit approval, with revocation available.

Practical Example

A model receives a dated official notice and produces a structured summary. The record stores the model version, input hashes, timestamp, limitations, and source links. A reviewer compares the summary to the notice; the workflow has no wallet, order, or signing permission.

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Related Terms

Agenticity

An informal way to describe how much an AI system can observe, plan, call tools, and act. It is not a standardized maturity score, a measure of trustworthiness, or permission to give an agent broad authority.

Perception Layer

The part of an AI workflow that collects and labels inputs such as blockchain records, market data, and public communications. It supplies context for review; it does not establish truth, identify people, predict prices, or justify an action.

LLM Reasoning

Using a language model to summarize, classify, or compare supplied information. Its output is probabilistic text, not verified market analysis, financial advice, or authority to execute a trade.

Prompt Engineering

The practice of designing model instructions, examples, context, and output constraints for a bounded task. It can make a workflow easier to evaluate; it does not make model output deterministic, correct, secure, or suitable for a financial decision.

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