Research
Cybersecurity & Threat Intelligence
Protecting data and the systems around it, from capture to disposal.
Purpose & context
Protecting data and the systems around it, from capture to disposal.
We examine security across the full data lifecycle: capture, transmission, storage, processing, analysis, sharing, retention and disposal. Threat intelligence helps us understand how real attacks develop across platforms, languages, identities and social-engineering channels, but the work does not stop at observation. We use that evidence to test systems, harden access, reduce exploitable paths, improve architecture and prepare teams to detect and respond. Where machine learning, generative AI or autonomous tools form part of a client's data environment, we extend the same security model to those components rather than treating AI as a separate practice.
A good fit when
- Sensitive data moves through several systems, teams, suppliers or collection channels and you need to understand where it can be exposed or manipulated.
- You need independent testing of access controls, applications, APIs, infrastructure or data-processing workflows rather than another generic security checklist.
- Threats such as phishing, account takeover, insider misuse, compromised integrations or emerging AI-enabled attack paths need to be translated into practical controls and response decisions.
Services in this area
Practical support for your priorities.
Data-pipeline security
Assess the security of data at capture, transfer, storage, processing, analysis, sharing, retention and disposal. Review trust boundaries, integrations, identities, encryption, access paths and opportunities for tampering or exfiltration.
Security engineering and adversarial testing
Test applications, APIs, networks, cloud configurations and identity boundaries within an authorised scope. Validate findings, harden access controls and prioritise architectural changes that remove or constrain attack paths.
Threat intelligence, detection and response
Research relevant phishing, fraud, social-engineering and intrusion patterns across the platforms and languages organisations actually use, then translate that intelligence into useful telemetry, detection logic, escalation criteria and incident-response exercises.
Information-security GRC
Support security and privacy risk assessments, control mapping, governance design and readiness work aligned where appropriate with ISO/IEC 27001 and ISO/IEC 27701. Where AI systems are actually in scope, extend this work to ISO/IEC 42001, the NIST AI RMF and applicable EU AI Act readiness questions such as risk classification, data lineage and accountability.
What we cover
Focused work. Useful evidence.
Threat-model the full data lifecycle from capture and transfer through processing, sharing, retention and disposal
Review and harden identity, authentication, authorisation, privileged access, service accounts and non-human identities
Conduct authorised application, API, network, cloud and infrastructure security assessments
Test data-pipeline integrity, third-party integrations and architectural trust boundaries
Research phishing, fraud, social-engineering and platform-specific attack patterns and translate them into practical controls
Design detection use cases, logging requirements, behavioural monitoring and incident-response playbooks
Run incident-response, recovery and adversarial exercises using controlled scenarios
Assess information-security and privacy governance against risk, ISO/IEC 27001 and ISO/IEC 27701 requirements
Where AI/ML is part of the data environment, assess data/model poisoning, prompt injection, unsafe retrieval, excessive agency, tool permissions, MCP integrations and other relevant AI-specific attack paths
What you take forward
Outputs designed to be used.
The final scope is agreed together. Depending on your needs, the engagement can include:
- Data-flow threat model and prioritised exposure map
- Validated security findings with reproducible evidence and affected trust boundaries
- Identity and access-control hardening plan
- Architecture and remediation backlog with priorities, owners and residual risks
- Detection, logging and incident-response requirements
- Security risk register and governance/readiness assessment
- Executive assurance summary linking technical findings to operational and governance risk
How we approach it
From the question to a practical next step.
Trace the data and trust boundaries
Start with how information is captured, transmitted, stored, processed, analysed, shared, retained and disposed. Identify the people, applications, devices, service identities, suppliers and interfaces that can affect its confidentiality, integrity or availability.
Test the controls, not just the documentation
Within agreed rules of engagement, validate authentication, authorisation, tenant separation, API behaviour, infrastructure configuration, data handling and detection controls. Use relevant threat intelligence to shape realistic scenarios while keeping testing bounded and authorised.
Harden and govern the system
Translate findings into architectural changes, access-control improvements, detection logic, response playbooks and governance actions. Map controls to relevant ISO, NIST or regulatory requirements when useful, and retest important fixes so assurance is based on evidence rather than policy alone.
Illustrative use case
What this can look like in practice.
An organisation collects information through field applications and partner APIs, processes it in cloud services and exposes selected outputs through dashboards and automated workflows. A security engagement could trace the end-to-end data path, test application and API authorisation, review service identities and privileged access, validate logging and exfiltration controls, exercise an incident scenario and produce a prioritised hardening and governance plan. If an AI component is used within that pipeline, its model, retrieval and tool-access risks are assessed as part of the same system rather than as a separate programme.
An example of the work—not a client case study.Reference frameworks
Explore independent guidance that can inform the scope and evaluation criteria for this work. These links do not imply accreditation or endorsement.
Planning an engagement
What should we prepare?
Bring the decision you need to make, a description of the relevant systems or data, known constraints and your timeline. We can agree the appropriate access and confidentiality arrangements before any sensitive information is shared.
How do we define success?
We agree specific questions and useful outputs at the start. Outputs such as data-flow threat model and prioritised exposure map are reviewed against the decisions you need to make. Limitations, dependencies and follow-up work remain visible in the handover.