Secure by engineering, compliant by consequence.
Application security, cloud hardening and compliance enablement (ISO 27001, KVKK, GDPR) — delivered by engineers who build systems, not just assess them.
- Assess
- Harden
- Embed
- Evidence
We’re builders first.
Security failures are engineering failures: the unrotated key, the over-permissioned role, the dependency nobody patched, the S3 bucket that was public for one afternoon and has been public ever since. None of those are exotic attacks. They are ordinary engineering debt that happened to have a security consequence, and they are found by the same discipline that finds every other kind of defect — reading the code, reading the configuration, and putting a gate in the pipeline so the mistake cannot be made twice.
So we secure systems the way we build them: as code, in the pipeline, with evidence. Least privilege is a Terraform module, not a policy paragraph. Dependency and secret scanning are CI jobs that fail a build, not a quarterly report. Threat modelling happens at design review, when changing the design still costs an afternoon. Done that way, compliance becomes a by-product of good engineering rather than an annual scramble: when your controls are already code and your evidence is already collected, an ISO 27001 or KVKK audit is a query over what you already do, and the data-protection measures your DPO signs are the ones actually running in production.
Findings come with pull requests, not just PDFs — and the same engineers who wrote the finding stay on the call when the fix breaks a test.
Four practices, one pipeline.
Every engagement draws on all four, weighted to what your estate actually needs. Nothing here is a separate consulting workstream — it all lands in the repositories and the delivery pipeline your teams already use.
Application security
Threat modelling, secure-coding review, dependency and secret scanning wired into CI — shifting security to where it’s cheap. We model the trust boundaries of a service before it is built, review authentication, authorisation and multi-tenancy isolation in the code rather than in a questionnaire, and put SAST, dependency and container scanning on the pull request so a finding arrives while the author still has the context. Findings that survive triage become tickets with an owner and a date, not a spreadsheet row.
Cloud hardening
IAM least-privilege as code, network segmentation, encryption and logging baselines across AWS, Azure and GCP. We start from what the account actually grants — not what the diagram says — then rewrite the roles, security groups, key policies and log sinks as Terraform, so the hardened state is the state the next deployment restores. Workload identity replaces long-lived keys; Kubernetes clusters get network policy, admission control and signed images.
Compliance enablement
Controls mapped to ISO 27001, KVKK and GDPR technical measures — mapped to evidence your auditors accept. We take the control set you are certifying against and answer, control by control, which system implements it, where the evidence is generated and how often. Data-residency, retention and erasure become properties of the pipeline. The output is a control matrix with links, not a binder that is stale the week after it is written.
Detection & response
Alerting on the signals that matter, incident runbooks, and tabletop exercises before the real thing. We instrument the events that indicate compromise — credential use from new geographies, privilege escalation, egress anomalies, data-access spikes — and tune out the noise that trains a team to ignore its own alerts. Then we write the runbook for each scenario, rehearse it with your engineers, and fix what the rehearsal exposes.
How the work runs, day to day.
Security gates that fail the build, not the launch.
A finding is cheap on a pull request and expensive in a release-gate review. So the gates live in CI: static analysis on changed code, dependency and licence checks against the lockfile, secret detection over the diff and the history, container and infrastructure-as-code scanning before anything reaches an environment. Each gate has a defined severity threshold, an owner and a documented exception path — because a gate nobody can bypass in an emergency is a gate somebody will disable permanently.
- Thresholds agreed with your engineering leads, so the start is not a wall of legacy findings
- Every gate exportable — it runs in your CI, under your credentials, after we leave
- Exceptions are time-boxed, recorded with a reason and an owner, and expire loudly
The permissions nobody has read since the migration.
Most cloud estates are secured by accident of history. A role was created wide to unblock a launch, a service account was shared because rotating it was hard, an old CI key still has administrator rights in an account nobody owns. We enumerate what is actually granted, compare it against what is actually used, and rewrite the difference as code — then hand you the review queue so the decisions are yours, with the evidence attached to each one.
- Effective-permission analysis across accounts, not a read of the policy documents
- Long-lived keys replaced with workload identity or short-lived credentials
- Every change lands as a reviewed pull request against your infrastructure repository
An incident is a rehearsed procedure, not an improvisation.
The difference between a contained incident and a bad quarter is usually the first hour. We write the runbooks for the scenarios your architecture actually permits — credential compromise, dependency compromise, data exposure, ransomware on a shared file estate — and then run them as tabletop exercises with the people who would really be woken up. What the rehearsal exposes is the real deliverable: the pager that goes to a person who left, the log retention that is shorter than the detection window, the backup nobody has restored from.
- Runbooks per scenario: detect, contain, eradicate, recover, notify — with named roles
- Notification timelines mapped to KVKK and GDPR obligations before you need them
- Tabletop findings tracked to closure like any other engineering defect
Three people have to be satisfied.
A register that ranks by exploitability, not by scanner severity.
A long list of findings is not a risk position. We rank by exploitability multiplied by impact in your architecture — an unauthenticated path to customer data outranks a high-severity CVE in a library that is never loaded — and report closure against that ranking, so the trend line means something at the board meeting.
- Risk register with owner, rank and target date per finding
- Closure trend by rank, against the agreed thresholds, at a rhythm agreed with you
- Residual-risk statements written in language a board can accept
Findings arrive as pull requests, in the tools you already use.
Engineers do not resent security; they resent a PDF that arrives six weeks after the code was written and blocks a release. Gates on the pull request, fixes proposed as diffs and a named engineer on your side of the review turn security into ordinary work — reviewed, tested and merged the same way everything else is.
- Gate configuration you can tune
- Fixes come with tests, so the regression is caught rather than remembered
- Secure-coding sessions on your own codebase, not on generic examples
Evidence that is generated, not assembled the week before.
Auditors ask two questions: does the control exist, and can you show it operated all year. Controls implemented as pipeline configuration answer both, because every run leaves a record. We map each control to the system that implements it and the artefact that proves it, then automate the collection so the answer is a query.
- Control matrix mapped to ISO 27001, KVKK and GDPR technical measures at once
- Evidence links per control, refreshed automatically rather than screenshotted
- Sub-processor, retention and residency positions documented alongside
What is standard and what is scoped per client.
How the work runs.
Assess
Architecture review, cloud-posture scan, and a risk register ranked by exploitability × impact. We read the code paths that handle authentication, authorisation and personal data; enumerate effective permissions across accounts; and check what your logs would actually tell you during an incident. The output is a ranked register with an owner proposed for each item — and an honest note where a finding is a design problem rather than a configuration one.
Harden
The top risks engineered out — IAM, secrets, patching, network paths — with changes shipped as code. Work is sequenced so the highest-ranked items move first and nothing waits on a full remediation programme. Each change is a reviewed pull request against your repositories, with a test or a scan result that demonstrates the risk is closed, and a rollback that your team has seen work.
Embed
Security gates in CI/CD, secure-coding practice with the team, and monitoring wired to response runbooks. Thresholds are agreed rather than imposed, exceptions get an expiry, and alerting is tuned against real traffic before anyone is put on call for it. Your engineers run the gates and the runbooks with us watching, until it is plainly their pipeline and not ours.
Evidence
Controls mapped to your compliance targets with collected evidence — audit-ready, continuously. Each control names the system that implements it and the artefact that proves it operated, and collection is automated wherever the platform allows. When the auditor arrives, the conversation is about the evidence in front of them rather than about what your team can reconstruct from memory.
What you keep.
- Risk register with exploitability-ranked findings
- Hardening changes delivered as reviewed pull requests
- CI security gates: SAST, dependency, secret and container scanning
- Incident-response runbooks and tabletop results
- Compliance control matrix with evidence links
When this is the right call.
Choose this when you have software you built or run and you need the security posture to move, not to be described. It fits teams heading into an ISO 27001 certification or a customer security review with a real estate behind them; cloud accounts that grew faster than their governance; products handling personal data under KVKK or GDPR where the technical measures have to be demonstrable; and organisations that have had an incident and want the causes engineered out rather than minuted. It pairs naturally with DevOps & Cloud when the pipeline itself needs rebuilding, with Cloud Migration when the target landing zone should be secure from the first workload, and with legacy re-engineering when the risk lives in a system nobody wants to touch.
Look elsewhere when what you need is a certificate and nothing else — a management-system consultancy will take you through the paperwork faster and cheaper, and we would rather point you there than sell engineering you do not need. Look elsewhere for deep offensive testing as a standalone product: we coordinate it with specialist partners and own the remediation, but we do not market ourselves as a red team. And this is the wrong engagement if no one on your side can accept a pull request — our findings arrive as code, and code needs someone to merge it.
Frequently asked questions
Services that carry the same standards.
Tell us what you’re building.
Describe what exists today and what you need. An engineer reads it and replies in writing with how we would approach it, and the model that fits.