Model privacy risk around data actions and effects on people

Bottom line: privacy problems can arise from authorized data processing, not only from breaches. Describe what the system does with data, who can be affected, how context changes the effect, and which design choices can reduce risk while preserving the intended service.

Source fact: what NIST IR 8062 introduces

NIST IR 8062 introduces privacy engineering and privacy risk-management concepts for federal systems. NIST identifies a common vocabulary as a way to improve communication about privacy risk and introduces privacy engineering objectives and a privacy risk model as two key components.

The publication’s federal context and introductory purpose are important boundaries. It provides a way to structure analysis; it does not determine every acceptable outcome or legal obligation.

What the source does not establish

The NIST report does not certify a system, calculate a universal privacy score, or prove that consent, a notice, encryption, or access control resolves every effect on individuals. Cybersecurity and privacy overlap, but they are not identical: a fully authorized use can still create an unwanted or disproportionate consequence.

This draft is not legal advice. Statutory definitions, rights, duties, sensitive categories, employee rules, biometric restrictions, public-sector obligations, and contract terms require context-specific review.

Applicability questions

What data actions does the system perform, including collection, generation, inference, transformation, combination, use, disclosure, retention, and deletion?

Which individuals and groups can be affected, including people who are not direct users?

What contextual factors—location, power relationship, expectation, accuracy, scale, duration, and ability to contest—change the effect?

Which effects can arise even when every operator is authorized and the system works as designed?

What technical and nontechnical choices can reduce the problematic action or its impact?

DSE recommendation: bring privacy into system design

The following steps are DSE recommendations based on the cited source.

Diagram data actions from the perspective of the people represented, not only the databases and network flows. Include inference, correlation, human review, automated decisions, sharing, and deletion.

Identify affected populations and context. Seek input from appropriate privacy, legal, operational, security, product, and stakeholder representatives.

Describe plausible problematic effects and organizational consequences with evidence and uncertainty. Do not label a speculative outcome as an observed fact.

Translate priorities into design requirements: minimization, separation, aggregation, accuracy checks, human review, access, transparency, correction, retention, deletion, monitoring, and response as appropriate.

Test the requirements against realistic workflows and misuse, including authorized but unexpected use. Record residual risk and decision authority.

Reassess when purpose, data sources, analytics, models, recipients, retention, scale, or affected populations change.

Verification and evidence

Retain the data-action map, affected-population analysis, assumptions and evidence, risk model, design requirements, decisions, test results, notices and user or operator workflows where applicable, residual-risk acceptance, and change triggers. Confirm that actual telemetry and retention match the approved design.

Official references

NIST IR 8062 — An Introduction to Privacy Engineering and Risk Management in Federal Systems — National Institute of Standards and Technology; finalized January 4, 2017

NIST Privacy Engineering Program — National Institute of Standards and Technology; living program page