SSQ.AISSQ.AI

Co-Intelligence Security

How people and AI work together

A way of working in which multiple professionals and AI agents investigate, analyse, develop and verify together.

People contribute expertise throughout the process, from defining the problem to improving the result—not only at the final approval stage.

How do people and AI actually work together?

Several people and AI agents take different roles and work together from defining the problem to verifying the result; permissions, approvals and records are handled in one layer above the work.

Reviewed

Control and verification Approval, stop Permissions Records, evidence approvals, permissions log People AI agents Tools Data and knowledge one shared workflow
People and AI agents are drawn as two dots each, as there are several of both. On the left, approvals and permissions come down from the layer above; on the right, the record of everything done goes up to it.

The parts that work together

A task draws on these parts together. They work towards the same goals in one workflow, and what is learned along the way builds up as security capability.

  • People and organisations

    Security experts who define problems, form hypotheses, experiment, analyse, build and judge. Your development, operations, audit and decision-making staff and our research and education partners take part in their own roles. Roles are not fixed as ‘the AI executes, people approve’; they are divided according to the difficulty of the task, the information and permissions involved, and what each participant can do.

  • Specialist tools

    Tools for attack, assessment, analysis and validation, used directly by people and also by AI agents.

  • AI models and security agents

    The external, internal and local models agreed and connected for each task, and role-specific security agents equipped with tools, knowledge, verification and collaboration.

  • Shared coordination

    One place that brings together the work split between people and agents, its status, context and results, evidence and approvals, so everyone can see who is doing what and it stays under control.

  • Data, knowledge and playbooks

    The expertise and experience, attack and defence techniques, playbooks, evaluation datasets and quality standards delivered to people, AI agents and models for use in a task.

  • Operation, control and verification

    Roles and permissions, approval and stop procedures, reasoning and evidence, quality and safety evaluation, and policy compliance.

  • Training and evaluation

    Hands-on and scenario training, controlled labs and challenges for people, and evaluation and improvement of AI agents' task performance and safe operation.

  • Services and partners

    Services that combine experts, tools and AI, working alongside partners' technology and data.

Six steps from connect to improve, repeated as needed

We don't simply add up what strong models, specialist tools, several AI agents and experienced people can each do. We design them to work together across six steps, to reach results none of them could reach alone. The steps are not a single pass: they repeat as the work needs, and run in parallel across the tasks people and agents have taken on.

CONNECT Tools and data ORCHESTRATE People and agents split, run in parallel OBSERVE Every step CONTROL Permissions VERIFY Results IMPROVE What was learned back again when more is needed into the next engagement Tools, data, knowledge, models Experthypotheses, tests AI agentresearch, analysis Expert + AI agentreproduction Record Stop condition Approved to proceed Expert check Confirmed False positive Knowledge, playbooks
Work split between people and AI agents runs side by side. Where verification shows more is needed, the work goes back a step and runs again; reviewed methods and knowledge carry into the next engagement, within the permissions agreed.
  1. Connect

    We connect the models, tools and data agreed for each engagement into one workflow.

  2. Coordinate

    Defining the problem, hypotheses and experiments, investigation, analysis and building are split between people and AI agents, and context and results pass through one place.

  3. Observe

    Each step is recorded: who did what, with which tool, and how far.

  4. Control

    Anything outside the set permissions is blocked, important actions need a person's approval, and work stops when an agreed stop condition is met.

  5. Verify

    Whether people, tools or AI found it, a finding is cross-checked and becomes a result only after an expert has filtered out false positives, and it is kept with its evidence. Where more is needed, the work goes back a step.

  6. Improve

    Within the permissions and scope agreed, reviewed methods and knowledge go into tools, playbooks and the way knowledge is delivered, and are used again in the next engagement.

Where the models available for a task are limited, we work in the same flow: splitting the work, running tools, and adding shared knowledge, cross-verification and human judgement. Why this matters: Sovereign Security AI

Where this leads

Scope

This page describes how we work. It is not a product or platform offered separately.

People & AI Collaboration (COLLAB) — the research domain that builds technology for people and AI to work better together; this way of working is how real engagements are run with it.