Step 1
Correlate
Join provider, identity, endpoint, network, cloud, code, and business-system evidence around the agent activity in question.
SECURITY FOR AI
Security changed shape when software started acting.
Pre-AI security protected people using software. Post-AI security must also govern software that reads, decides, calls tools, and acts. Provider controls are necessary, but only a cross-estate evidence architecture can prove what an agent saw, decided, called, changed, and handed back to a human.
WHY NOW
Approval is not control when software can act.
Exploit windows are compressing while agent sessions and tool access are lengthening. Boards are approving AI adoption faster than security teams can prove what agents actually do with identities, data, code, and business systems. The organisations that adopt safely are the ones that can reconstruct every agent action chain: input, source, model output, tool call, approval, action, and correction.
TWO LINKED JOBS
AI for Security and Security for AI are one operating problem.
The next security era has two foundational jobs. They are joined by the same evidence architecture: if you cannot reconstruct what an agent saw, decided, called, and changed, you cannot govern either.
AI for Security
Use governed agents to compress investigation and response work, with identity, tool scopes, policy, approval, audit, and rollback under control.
Security for AI
Observe and control AI providers, agents, identities, tools, data access, code, and the actions those systems take across your estate.
FIVE CISO PRIORITIES
Five decisions now need evidence.
Agent risk is an identity, supply chain, data, and action problem. Each priority becomes a question your evidence has to answer before the board signs off.
Adoption governance
Which agents, providers, and tools are approved, and can you prove who used what under which policy?
Agentic supply chain
Which models, packages, plugins, and knowledge sources sit on the path from prompt to production change?
Identity and privilege
Which identities, tokens, and tool scopes can an agent exercise, and who can approve escalation?
Data exposure
What sensitive data entered a model context, left a controlled system, or moved through an AI workflow?
Incident reconstruction
Can responders reconstruct the full action chain and interrupt or reverse agent behaviour under policy?
Provider controls are pieces of the puzzle, not the whole investigation.
Each provider contributes real native controls and audit evidence, and none of them can see your whole estate. Provider logs alone cannot reconstruct a full agent action chain. SenseOn correlates what each source contributes with the evidence around it, so every row ends in a decision you can actually make.
Native evidence
Contributes IAM and model access controls, guardrails, CloudTrail API events, and optional invocation logging. Outside its view: cross-provider activity, endpoint and network context, and the downstream business actions an agent takes.
SenseOn correlation
SenseOn correlates Bedrock invocation and identity evidence with cloud, network, endpoint, and code telemetry. Decision enabled: was this agent action expected, and what should be contained?
AWS Bedrock
Contributes IAM and model access controls, guardrails, CloudTrail API events, and optional invocation logging. Outside its view: cross-provider activity, endpoint and network context, and the downstream business actions an agent takes.
SenseOn correlates Bedrock invocation and identity evidence with cloud, network, endpoint, and code telemetry. Decision enabled: was this agent action expected, and what should be contained?
Native evidence
Contributes SSO, SCIM, and role controls, plus enterprise audit logs covering user actions, system events, and data access. Outside its view: activity beyond the managed workspace, downstream tool effects, and cross-estate lineage.
SenseOn correlation
SenseOn correlates Claude activity with identity, endpoint, code, network, and SaaS evidence. Decision enabled: who used Claude, what data or code was involved, and what changed next?
Anthropic / Claude
Contributes SSO, SCIM, and role controls, plus enterprise audit logs covering user actions, system events, and data access. Outside its view: activity beyond the managed workspace, downstream tool effects, and cross-estate lineage.
SenseOn correlates Claude activity with identity, endpoint, code, network, and SaaS evidence. Decision enabled: who used Claude, what data or code was involved, and what changed next?
Native evidence
Contributes workspace identity and administration, compliance logs and APIs, and API data controls; platform retention is time-bounded unless exported. Outside its view: personal accounts, non-OpenAI tools, and downstream actions.
SenseOn correlation
SenseOn retains provider evidence on your terms and correlates it with the rest of the estate. Decision enabled: is this event isolated provider use, or part of a wider incident?
OpenAI / ChatGPT Enterprise
Contributes workspace identity and administration, compliance logs and APIs, and API data controls; platform retention is time-bounded unless exported. Outside its view: personal accounts, non-OpenAI tools, and downstream actions.
SenseOn retains provider evidence on your terms and correlates it with the rest of the estate. Decision enabled: is this event isolated provider use, or part of a wider incident?
Native evidence
Contributes tenant policy, identity, information protection, and DSPM and DLP audit of prompts and resources accessed when configured. Outside its view: non-Microsoft providers, endpoint and network behaviour, and code and runtime outcomes.
SenseOn correlation
SenseOn correlates Microsoft evidence with external AI, device, network, code, and business systems. Decision enabled: did protected data move through AI, and was the resulting action authorised?
Microsoft Copilot + Purview
Contributes tenant policy, identity, information protection, and DSPM and DLP audit of prompts and resources accessed when configured. Outside its view: non-Microsoft providers, endpoint and network behaviour, and code and runtime outcomes.
SenseOn correlates Microsoft evidence with external AI, device, network, code, and business systems. Decision enabled: did protected data move through AI, and was the resulting action authorised?
Native evidence
Contributes enterprise AI policy and access controls, plus agent audit events with the initiating user, session, and action. Outside its view: local prompt and session detail, package and runtime behaviour, and cloud and SaaS effects.
SenseOn correlation
SenseOn correlates agent audit events with repo, package, CI/CD, endpoint, identity, and runtime evidence. Decision enabled: did an agent introduce or propagate risky code, dependency, or credential use?
GitHub Copilot
Contributes enterprise AI policy and access controls, plus agent audit events with the initiating user, session, and action. Outside its view: local prompt and session detail, package and runtime behaviour, and cloud and SaaS effects.
SenseOn correlates agent audit events with repo, package, CI/CD, endpoint, identity, and runtime evidence. Decision enabled: did an agent introduce or propagate risky code, dependency, or credential use?
THE SENSEON ARCHITECTURE
One system from evidence to governed action.
SenseOn is the complete three-layer system, not a middle layer between your tools. The Data Fabric feeds the Intelligence Fabric, and the Agent Control Plane governs everything that acts on them.
Data Fabric
Ingests, shapes, and retains source-linked evidence from identity, endpoint, network, cloud, SaaS, AI providers, code, and business systems.
Intelligence Fabric
Joins entities, sequences, detections, and prior context so investigations stay source-linked as the questions multiply.
Agent Control Plane
Governs every agent that works a case, with Horus orchestrating and Resolve investigating, under identity, tool scopes, policy, approval, audit, timeout, escalation, and rollback.
HOW SENSEON WORKS A CASE
Four steps from signal to decision.
Every case follows the same governed workflow, and the decision layer flows from these four steps. The Evidence Pack is what your team, your auditors, and your board use to decide what to fund, govern, contain, or expand.
Step 2
Fill in any Gaps
Identify missing sources in the chain and bring them into view, so the record is complete rather than a partial provider log.
Step 3
Investigate
Work the case on source-linked evidence, following each question from model output to tool call to downstream effect.
Step 4
Evidence Pack
Produce an inspectable record of what the agent saw, decided, called, changed, and handed back to a human.
THE FOUR-WEEK ASSESSMENT
Prove one evidence chain in four weeks.
One real agent workflow, your own telemetry, a pass or fail reconstruction test, and an executive readout. Bounded scope, inspectable outputs, no rip-and-replace.
Week 1: Source map
Inventory AI providers, agent workflows, identities, and tools, and map the evidence each already produces. Output: a source coverage map and the reconstruction gaps.
Week 2: Bounded workflow
Select one agent workflow that crosses trust boundaries and define the chain-of-custody questions. Output: workflow scope, pass or fail criteria, and a response boundary.
Week 3: Evidence chain
Correlate provider, identity, endpoint, network, and code evidence for the selected workflow. Output: an inspectable chain from input to correction.
Week 4: Executive readout
Present the risks found, the remaining gaps, and the recommended governed scope. Output: a decision pack for funding and next steps.
GOVERNANCE PROOF
Governed action you can show an auditor
SenseOn agents work under human governance with an auditable record of every action, and the programme behind the platform is independently certified and tested.
BSI ISO 27001
Certified security management programme
SE Labs AAA
Independently tested detection quality
AV-Comparatives
Independent endpoint protection testing
Gartner Peer Insights
Rated 4.9/5 by reviewers
World Economic Forum
Technology Pioneer
NEXT STEP
Bring one agent workflow. Leave with an evidence-backed control plan.
We will map your AI providers and agent workflows, show where the evidence chain breaks, and scope a four-week assessment on your own telemetry.