Our Editorial Approach

Our Editorial Principles

Our editorial approach is guided by several principles intended to make AI security education clear, useful, and responsible.

Clarity without oversimplification

We explain security concepts in accessible language while preserving the distinctions necessary to understand how systems behave and how safeguards work.

Evidence before conclusions

We distinguish documented research findings from interpretation, design recommendations, hypothetical examples, and proposed tests.

Security boundaries matter

AI systems can introduce new complexity into established security relationships. We focus on understanding authority, trust boundaries, enforcement, accountability, and the evidence needed to evaluate safeguards.

Two perspectives, one instructional foundation

Our Executive and Technical explanations are developed from the same underlying research and central lesson, while addressing the questions most relevant to each audience.

Transparency about limitations

We acknowledge uncertainty, testing limitations, and the conditions under which research findings may or may not apply.

How We Research

Our research process begins by identifying developments that offer meaningful opportunities to improve readers' understanding of AI security.

We examine original research, technical publications, incident analyses, security guidance, and other substantive sources.

Research is evaluated for relevance, credibility, instructional value, and the strength of the available evidence.

We pay particular attention to what a source actually demonstrates, the conditions under which findings were observed, and the conclusions that cannot reasonably be drawn from those findings.

For example, a security control demonstrated in a laboratory evaluation may provide useful evidence about that particular test without establishing that the control will work identically in every production environment.

How Research Is Reviewed

Research review considers:

• Whether the source supports the stated claims.
• Whether findings are accurately attributed.
• Whether important qualifications and limitations are preserved.
• Whether the proposed article offers a distinct and useful teaching opportunity.
• Whether the topic can be explained responsibly to both Executive and Technical readers.

A research opportunity is reviewed and selected before it advances into article development.

Research discovery does not automatically result in publication.

How We Develop and Review Articles

Once a research topic is selected, we develop an instructional plan that identifies the central lesson, essential concepts, supporting evidence, and practical questions readers should understand.

The instructional plan establishes a common foundation for two explanations of the same subject.

Executive Explanation

The Executive version focuses on business significance, responsibility, risk, governance, and the evidence leaders should request when evaluating AI security decisions.

Technical Explanation

The Technical version examines underlying mechanisms, system behavior, authorization, enforcement, trust boundaries, verification, and remaining limitations.

Both explanations are intended to communicate the same underlying security lesson without requiring readers to have the same technical background.

How Writing Is Reviewed

Article reviews consider:

• Accuracy and support for factual claims.
• Consistency with the approved research and instructional foundation.
• Clear distinctions between documented findings and illustrative scenarios.
• Appropriate depth and terminology for each audience.
• Consistency between the Executive and Technical explanations.
• Practical usefulness of the conclusions and questions presented.
• Appropriate attribution and preservation of evidence limitations.

Complete article drafts are reviewed before being approved for the next production stage.

Material corrections or changes to the central teaching lesson require further editorial review.

How We Develop and Review Visuals

Visuals are an instructional part of AI Security Explained articles.

Our diagrams, Learning Maps, illustrations, and comparison graphics are intended to clarify relationships, explain mechanisms, and reinforce understanding—not simply decorate a page.

Visual planning begins by identifying where a graphic would materially improve the explanation.

Each proposed visual is evaluated against its intended teaching objective and its relationship to the accompanying article.

How Visuals Are Reviewed

Visual reviews consider:

• Alignment with the article's central lesson.
• Accuracy of security concepts and technical relationships.
• Correct representation of authority, enforcement, and trust boundaries.
• Appropriate distinction between hypothetical designs and documented implementations.
• Clear labeling and readable visual hierarchy.
• Consistency with the accompanying Executive or Technical explanation.
• Accessibility, including descriptive text where appropriate.
• Preservation of source qualifications and technical limitations.

Learning Maps

Some articles use a Learning Map to connect several important concepts to a central security question. These maps help readers understand how concepts relate to one another and provide a reference they can revisit after reading the article.

Learning Maps are intended to support conceptual understanding, not imply that every security problem follows a fixed sequence of steps.

Visuals are planned and reviewed separately from written drafts.

Final visuals are reviewed before being incorporated into an article's publication package.

How We Use AI

AI Security Explained uses AI-assisted tools to support research organization, instructional planning, drafting, visual development, and publication preparation.

These tools can help organize complex information, compare explanations, and develop educational material efficiently.

However, AI-generated text, analysis, and illustrations are not treated as authoritative evidence simply because an AI system produced them.

Source material must support factual claims. Illustrative examples must be distinguished from documented events, and proposed technical safeguards must not be represented as independently validated without appropriate evidence.

Human editorial oversight remains responsible for deciding which research opportunities to pursue, whether article explanations are suitable, which visuals are accepted, and when material is ready for publication.

AI assists in developing the content. It does not replace responsibility for the content.

How We Prepare Articles for Publication

Before an article is published, its approved written explanations, instructional visuals, source references, and supporting information are assembled into the website's established article format.

The publication review is intended to verify that the presented article matches its approved materials and that important instructional and accessibility elements are functioning correctly.

Publication Review

Our editorial standards call for the following intended release checks:

• Completeness of both Executive and Technical versions.
• Correct placement and versions of instructional graphics.
• Accuracy of article classifications and terminology.
• Working source references and glossary links.
• Readable and accessible presentation.
• Navigation between the two explanations.
• Consistency with the publication's established visual design.

Publication Authorization

Editorial approval of content does not automatically authorize publication.

The completed article is reviewed as a website experience before a separate publication decision.

Sources, Limitations, and Corrections

Sources

We aim to provide references that allow readers to examine the research and technical material underlying an article.

Where relevant, we distinguish original research, vendor guidance, independent evaluation, and instructional interpretation.

A source's findings should not be presented as more general or conclusive than the evidence supports.

Hypothetical Examples

Hypothetical scenarios are used to make security concepts easier to understand.

These examples should be clearly identified as illustrative, rather than presented as actual incidents or verified deployment outcomes.

Similarly, suggested tests and evaluation procedures are recommendations unless their execution and results are specifically documented.

Corrections and Updates

AI security continues to evolve, and published explanations may occasionally require clarification, correction, or updating.

We aim to correct material inaccuracies and revise explanations when credible new evidence changes the conclusions readers should draw.

Substantive updates should be identified appropriately, including an updated date where relevant.

Reader Feedback

Readers who identify a possible error, unclear explanation, or outdated reference are encouraged to contact the publication.

Editorial Responsibility

AI Security Explained is published by Paul Oyola.

Research, drafting, and visual production may involve AI-assisted tools, but editorial selection, content approval, and publication decisions remain under human responsibility.

Our goal is not to claim certainty in a rapidly evolving field.

It is to help readers distinguish what is known, what is proposed, what remains uncertain, and what evidence is needed to make informed security decisions.

Clear explanations. Visible boundaries. Informed decisions.