We support organisations across these sectors. Sector context changes the regulatory obligations, the data at stake and the tolerance for downtime — but the engineering discipline underneath stays the same.
Sectors
We describe the security pressures typical of each sector and the
services that most often apply. Nothing on this page should be read as
a claim of prior client work in that sector — where we are able to
reference specific engagements, we will do so with the client's
written permission.
SaaS & Technology
Multi-tenant platforms shipping continuously, where a security review can decide whether an enterprise deal closes.
Typical pressures
Proving tenant isolation to enterprise buyers and their auditors
Keeping release velocity while adding security gates to CI/CD
SOC 2 or ISO 27001 obligations arriving before a security team exists
Broad cloud estates accumulated faster than the guardrails around them
Security questionnaires consuming engineering time every quarter
How we support teams here
DevSecOps rollout that adds controls without slowing delivery
Application security testing and multi-tenancy authorisation review
Cloud security baselines for AWS and Google Cloud
SOC 2 and ISO 27001 readiness with automated evidence collection
Customer security questionnaire and trust documentation support
AI Product Companies
Teams building on foundation models, agents and retrieval pipelines, where the security model has to keep pace with the architecture.
Typical pressures
Prompt injection and jailbreaks against user-facing AI features
Agents with tool and API access widening the blast radius of a text flaw
Retrieval pipelines quietly crossing tenant and permission boundaries
Customer and regulator questions about training data and model providers
No established governance path for approving new AI use cases
How we support teams here
AI threat modelling for LLM, RAG and agentic architectures
Prompt injection defence and tool-use authorisation design
Data classification and provider data-handling rules
AI red teaming and adversarial evaluation in CI
AI governance framework aligned to NIST AI RMF and ISO/IEC 42001
Financial Services & Fintech
Regulated environments where cloud adoption, payment data and audit scrutiny all apply at once.
Typical pressures
Overlapping obligations across PCI DSS, ISO 27001, SOC 2 and regulator expectations
Demonstrating segmentation and data protection in a cloud estate
Strong requirements for access control, logging and evidence retention
Third-party and vendor risk across a wide integration surface
Incident response expectations measured in hours, not days
How we support teams here
Cloud security architecture with segmentation and data-perimeter design
Identity and access management review with least-privilege enforcement
PCI DSS and ISO 27001 gap assessment and remediation planning
Detection engineering, log retention and SIEM integration
Incident response planning and tabletop exercises
Healthcare & Life Sciences
Organisations handling sensitive personal and clinical data, often across a mix of legacy systems and new cloud services.
Typical pressures
Protecting sensitive personal and health data across hybrid estates
Privacy obligations that vary by jurisdiction
Third-party processors and integrations expanding the data footprint
Legacy applications that cannot be patched on a modern cadence
Growing interest in AI tooling applied to sensitive records
How we support teams here
Data classification, minimisation and encryption strategy
Cloud security architecture with strict data-perimeter controls
Third-party and vendor due-diligence workflows
Compensating controls and segmentation for legacy systems
Secure AI adoption review before sensitive data reaches a model
E-commerce & Retail
High-traffic consumer platforms where payment data, account security and automated abuse all sit on the critical path to revenue.
Typical pressures
Cardholder data scope and PCI DSS obligations
Account takeover, credential stuffing and bot-driven abuse
A large third-party script and integration surface on the storefront
Peak-season availability pressure competing with change control
Fraud and business-logic abuse that scanners do not detect
How we support teams here
PCI DSS gap assessment and scope reduction
Application security testing including business-logic and abuse cases
Incident readiness planning ahead of peak trading periods
Startups & Scale-ups
Fast-moving teams that need a security baseline proportionate to their stage — and a plan for the next one.
Typical pressures
No dedicated security owner; responsibility sits with an engineering lead
Security work triggered by a blocked deal or an investor question
Cloud and SaaS estates growing faster than any access review
Limited budget for tooling, so control choices have to count
Founders needing to know what is genuinely urgent versus what can wait
How we support teams here
Pragmatic security baseline sized to your stage and risk
Prioritised roadmap distinguishing must-fix from later
Cloud guardrails delivered as reusable Infrastructure as Code
SOC 2 readiness without a documentation-heavy programme
Advisory support on retainer instead of a full-time hire
Cloud-Native Enterprises
Large estates running Kubernetes, microservices and multi-account cloud, where consistency matters more than any single control.
Typical pressures
Multi-account, multi-project estates with inconsistent baselines
Kubernetes platforms hardened differently by each team that built one
Identity sprawl across cloud IAM, SSO and workload credentials
Detection coverage that varies by account, region and workload type
Platform teams asked to own security outcomes without a mandate
How we support teams here
Landing zone and guardrail design enforced through policy as code
Kubernetes hardening standards applied consistently across clusters
Identity consolidation and least-privilege programmes
Detection coverage assessment and SIEM integration
Platform security operating model with clear ownership
Professional & Business Services
Firms holding client data under contractual security obligations, where trust is the product.
Typical pressures
Client contracts imposing specific security and audit requirements
Confidential client data spread across SaaS collaboration tools
Phishing and business email compromise targeting client communications
Limited internal IT and security capacity
Growing use of AI assistants against confidential material
How we support teams here
Security programme design proportionate to contractual obligations
SaaS and identity security review with data-loss prevention
Security awareness and phishing simulation programmes
Incident readiness planning and tabletop exercises
AI acceptable-use policy and safe-adoption guidance
Not listed here?
Sector matters less than architecture. If you run cloud infrastructure, ship software or are adopting AI, the work is likely to be recognisable — tell us what you are dealing with.