The Integrated Intelligence Sense-Making Model

The Integrated Intelligence Sense Making Model is a practical way of organising how analysts and agencies turn growing volumes of information into decision-relevant understanding. It brings several established approaches into one connected ‘systems’ approach: Object-Based Intelligence and Production, a situation lens, Activity-Based Intelligence, Target Systems Analysis, analytic judgement, ongoing collection and decision feedback.

Its central claim is simple: these single approaches answer different questions about the same problem and work best together, when they share evidence, challenge one another and preserve uncertainty. The model is a synthesis of doctrine, methodologies and practice – it is not a new intelligence cycle. It is a way to see the relationships that a linear production process can hide.

Why an integrated model is needed

Modern intelligence environments contain more sensor data, reporting, imagery, messages, transactions and open-source material than analysts can examine. The Australian Defence Science and Technology group describes the challenge as translating large volumes of uncertain, disparate data into resilient situational insight, while retaining human judgement, explainability and contestability. US and NATO doctrine likewise stress fused, all-source intelligence, continuing collection management and close integration with planning and operations.

Most organisations already use or participate in parts of this model. They resolve identities, assess situations, detect patterns, study systems, manage collection and brief decisions. The difficulty is that these activities may sit in different teams, tools or production routines. Therefore, findings can be duplicated, context can be lost and a confident-looking conclusion or assessment can become detached from its evidence base.

What the model changes

The model shifts the focus from a sequence of products to a shared and continuously tested knowledge environment. Observations, reports and data are not treated as conclusions. Objects, activities, situations and system relationships remain assessable claims linked to their sources, time and confidence ratings. Analysts can move between lenses as the question changes, rather than forcing every problem through the same ‘process-driven’ method.

• Start with the decision required or the intelligence need, but allow evidence to reshape the question.
• Organise knowledge so that different teams can reuse it without hiding disagreement, contestation or source limits.
• Treat collection, analysis, action and assessment as connected feedback, not as separate stages in a linear process.
• Keep analytic judgement visible across the whole system rather than locating it only in a final product.

A synthesis rather than a doctrine

The component approaches in this Model are drawn from different intellectual and professional traditions. Object-Based Production is an information and production architecture; Activity-Based Intelligence is an analytic approach to interactions and change; Target Systems Analysis studies functions and dependencies; and situation theory is a formal account of context-dependent information. The model does not claim that these are equivalent layers. It uses them as complementary lenses, with the situation lens drawing inspiration from situation theory. Each of the lenses will be discussed separately, in detail, as standalone IIP discussion papers in this Series.

The integrated model

At the centre is a shared evidence and knowledge foundation or substrate. Around it sits four lenses, each with a distinct organising question at its centre. Analytic judgement frames and tests the evidence and knowledge. Collection fills gaps and challenges assumptions. Decisions and action create effects that must be observed, assessed and fed back into the knowledge base.

Figure 1: Integrated Intelligence Sense Making Model - A synthesis (not assertion) of doctrine, theory and practice

Four complementary lenses

Why the arrows run both ways

This model is not linear – it does not represent a ‘step-by-step’ process. For example, an activity pattern may reveal a previously unknown object. A changed situation may make an established pattern irrelevant. A system dependency may create a collection priority. New collection may split one object into two identities or weaken a causal claim. Action may alter the system being analysed. Bidirectional relationships between the lenses represent analytic challenge, updating and learning, not just data transfer in a process-driven way.

Judgement, Validation and epistemic control

Tools can correlate, retrieve and visualise at scale, but they do not remove the need for accountable judgement. Analysts still decide which questions matter, whether observations refer to -the same entity, what alternatives deserve testing, how likely an explanation is and what implications should be communicated. Published Australian policy and doctrine require assessments to be independent, clear, timely, accountable and rigorous. The model places that responsibility on every lens, rather than just once at the end of a pipeline.

Epistemic control is the model's safeguard against ‘reusing’ errors and allowing them to ‘become fact’. Every important assertion should retain provenance and lineage, source reliability, information credibility, confidence, temporal validity and the identity of the assessing process or analyst. Contradictions and alternatives should remain visible. Access controls may restrict source detail, but they should not turn a qualified assessment into an unexplained fact.

How the model supports operations

Used as a working methodology, the model helps teams move from a decision need to a set of testable questions. It allows them to select the right lenses, identify knowledge gaps and update shared understanding as events unfold. It can support strategic assessment, investigations, targeting, situational warning, regulatory intelligence, risk, commercial intelligence and other domains. Its value is not a particular software platform; it is disciplined reuse of knowledge with context and uncertainty intact, traceable and validated.

• For analysts it reduces repeated discovery work and makes assumptions easier to test.
• For collectors it turns gaps and competing explanations into more focused requirements.
• For leaders it connects conclusions to evidence, confidence, alternatives and operational effects.
• For agencies it provides a common frame for collaboration across data, disciplines and levels of analysis.

Limits and implementation risks

Integration can spread error as quickly as insight. Premature identity resolution, rigid taxonomies, stale objects, hidden model assumptions, automation bias and broad access to sensitive data can all undermine the approach. A visually persuasive network or system map is not proof of causation. Nor should the model be used to collapse legal authority, policy, collection disciplines or professional accountability into one technical platform.

Implementation should therefore begin with governance and tradecraft: common community agreed definitions, assertion-level provenance, rules for ageing and correction, access controls, audit trails, alternative analysis and clear ownership. Technology should support these practices, not define them. Success should be measured by better questions, faster correction, reduced duplication and more explainable decisions rather than by the volume of data connected.

What the model means in practice

The Integrated Intelligence Sense Making Model offers an overarching way to connect the specialised lenses; in this IIP Series we will explore the lenses in depth through a number of follow-on discussion papers. The Model recognises that intelligence understanding is provisional, collaborative and shaped by continual feedback. By linking evidence, objects, context, activity, systems and decisions while preserving uncertainty, it provides analysts and agencies a practical foundation for making sense of complexity, without pretending that complexity has disappeared.

Selected references

• Australian Department of Defence, Defence Intelligence Organisation Mandate and Intelligence Controls
• Australian Department of Defence Science and Technology Group, Intelligent Decision Superiority and Intelligence Analysis
• Australian Defence Force, ADDP 2.0 Intelligence, Australian Defence Doctrine Publication, Intelligence and Security Series.
• Australian Defence Force, ADDP 3.14 Targeting, Edition 3 - intelligence support, target systems analysis, collection priorities and assessment.
• NATO Standardisation Office, AJP 2 Allied Joint Doctrine for Intelligence Counter Intelligence and Security, editions A and B.
• United States Joint Chiefs of Staff, JP 2 0 Joint Intelligence.
• United States Air Force, AFDP 2 0 Intelligence, 1 May 2026 - Activity Based Intelligence and intelligence fusion.
• RAND Corporation, Developing Taxonomies to Support Object Based Production, RRA3152 1, 2024.
• Jon Barwise and John Perry, Situations and Attitudes, MIT Press, 1983.
• Peter Pirolli and Stuart Card, The Sensemaking Process and Leverage Points for Analyst Technology, Proceedings of the International Conference on Intelligence Analysis, 2005.
• Donella H Meadows, Thinking in Systems A Primer, Chelsea Green, 2008.

Publication Statement

AI tools were used to assist with structuring and editing for clarity. All views expressed are those of the author(s) and are offered to support open, respectful discussion. The Institute for Intelligence Professionalisation values independent and alternative perspectives, provided safety, privacy, and dignity are upheld.