Security that ships inside every release, not around it
Application Security
Most application vulnerabilities are not exotic; they are the same handful of defect classes shipped repeatedly because nothing in the pipeline was built to catch them earlier. This practice treats the software development lifecycle itself as the surface to secure, from a threat model sketched before a feature exists to a scanning gate that runs on every merge. It covers the whole application, deliberately wider than a single API contract or a one-time exploit attempt.
How security gets built into the pipeline itself
Scanning thresholds, threat modelling and the triage process behind a merge gate.
90 secSample frame · video pending01Before wiring a scanning gate
SAST and DAST catch different things entirely.
Teams often pick one and assume it covers the other. Four questions clarify what each actually finds in your specific pipeline.
Do you need findings before or after a build runs?
Static analysis reads source code directly and can run on every commit. Dynamic testing needs a running application, so it fits later in the pipeline.
Are you worried about code flaws or runtime behaviour?
Static analysis catches injection patterns and unsafe function use in the code itself. Dynamic testing catches how the running application actually responds to real requests.
How much noise can your team absorb per release?
Static analysis tends toward more false positives that need tuning. Dynamic testing against a live environment usually reports fewer, more confidently exploitable issues.
Does the application have complex runtime configuration?
A configuration-heavy deployment benefits from dynamic testing, because a static scan cannot see how environment variables change behaviour in production.
| SAST | DAST | |
|---|---|---|
| Runs against | Source code | A running application |
| Pipeline stage | On every commit | After a build is deployed |
| Finds | Unsafe code patterns | Exploitable runtime behaviour |
| Best fit | Early, frequent feedback | Realistic pre-release confirmation |
02Agreed before the gate goes live
The ledger that keeps a scanning gate honest
Application security drifts when a gate quietly starts blocking releases nobody agreed it should block. Here that gets settled upfront: what you supply, what gets configured, what comes back, and which calls remain entirely your team's to make.
- Your side
You bring
- The current CI/CD pipeline and its build stages
- The languages and frameworks the codebase actually uses
- Any prior security findings sitting unresolved in a backlog
- Our side
We build
- Static and dynamic scanning wired into the pipeline itself
- Thresholds tuned so real findings surface above noise
- A triage process for what blocks a merge and what does not
- Your side
You receive
- A pipeline that fails a build on a real finding
- A cleared or scheduled backlog of prior findings
- A dashboard tracking finding volume release over release
Never leaves you
- The codebase, its architecture and its release cadence
- The final call on any exception to a scanning gate
- Ownership of the pipeline once the gate is live
Where the line sitsWe tune the gate; you keep the authority to override it. A scanning gate nobody can ever bypass gets disabled within a quarter out of frustration.
03From baseline to a live gate
Four stages, each closing on evidence
A scanning gate that fails usually skipped one of these stages before going live. This system closes each one with something the engineering team can already see working.
Baseline the existing risk
An initial scan across the codebase and its dependencies establishes what already exists, so new findings can be told apart from inherited ones.
Where the codebase stands before anything changesConfigure and tune the gate
Scanning rules get tuned against your specific languages and frameworks until the signal-to-noise ratio is something a team will actually respect.
Tuned to your codebase, not a default rulesetDefine the triage and override path
A clear process for what blocks a merge, what gets scheduled, and who can grant an exception keeps the gate from being quietly disabled.
A decision the team can trust under deadline pressureShip the gate and its dashboard
Finding volume and remediation time become visible release over release, so the gate's value shows up in a number, not a promise.
Visible in the same place engineers already look
04What actually moves the number
Four drivers, priced from a baseline scan
Application security work is priced from an initial baseline, because these four factors change the ongoing effort by multiples.
Size and age of the codebase
A five-year-old codebase with accumulated findings takes longer to baseline than a service built in the last year.
Number of languages and frameworks in use
Each additional language or framework in the stack needs its own tuned ruleset rather than a shared default.
How many features need a threat model
A single payments feature is a narrower scope than threat modelling every feature touching regulated data across the product.
Depth of dependency remediation required
Flagging vulnerable dependencies costs less than actually upgrading a legacy dependency tree with breaking changes to absorb.
05Evidence and proof
Defects caught before they ever reached a release.
Application security proof is a falling defect count and a gate the team stopped fighting. Every record here waits for written client approval before production.
Explore case studiesA scanning gate that cut shipped defects without slowing releases.
Pillar Learning shipped features weekly with no automated security check in the pipeline. A tuned CI gate caught injection-class defects at merge time without adding meaningful build delay.
- Injection-class defects reaching production
- Down to zero
- Added build time per merge
- Under two minutes
A dependency audit that surfaced a critical, unpatched library.
Aster North had never inventoried its open-source dependencies across a decade of accumulated code. The audit found a critical vulnerability in a transitive dependency nobody had tracked.
- Dependencies inventoried
- Over 600
- Critical vulnerabilities found
- 1
Our own engineers stopped arguing with the gate once the noise dropped. That took real tuning, not a default config.
Sample · pending client approval
06Read this first
When an ongoing practice is the wrong answer
Standing up a continuous scanning practice for a codebase that is being retired within the year is effort spent in the wrong place. The exits are marked plainly below.
- Penetration Testing
You need one exploitation attempt, not a standing practice
A single, time-boxed test against a defined scope fits better than an ongoing lifecycle commitment.
- API Security
The concern is only your API surface
If the whole codebase is not in question, a narrower contract-layer test answers it faster and at lower cost.
- Hire DevSecOps Engineers
You need security engineers, not a delivery layer
If your team already owns the security roadmap and just needs hands, team extension fits better than a commissioned practice.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Scoped rollout | One pipeline, one gate to stand up | Thresholds and override policy | Configuration and baseline |
| Ongoing practice | Security needs to scale with releases | Roadmap priorities each cycle | Tuning, triage and reporting |
| Team extension | Your leads own the security roadmap | Standards and architecture | Named engineers and continuity |
07Straight answers
Asked before a scanning gate goes live.
01Is this the same as API security?
No. This practice covers the entire codebase across the whole development lifecycle. API security narrows specifically to the contract layer other systems call.
02Do you integrate with our existing CI/CD tooling?
Yes, scanning typically wires into whatever pipeline you already run rather than requiring a migration to a new toolchain. Configuration is tuned to your existing build stages.
03Will a scanning gate slow down our releases?
A properly tuned gate adds minutes, not hours, to a build. Most of the early effort goes into cutting false positives so the team never wants to bypass it.
04Do you threat model every feature, or only some?
Only the ones carrying real risk, such as payments or regulated data, unless your team wants broader coverage. Threat modelling every trivial feature wastes effort better spent elsewhere.
05How do you handle dependency vulnerabilities we can't immediately fix?
Findings get ranked by exploitability, and some accepted risks get documented rather than forced into an unsafe rushed upgrade. Not every vulnerable dependency needs an emergency fix.
06Can you work with a team that already has a security engineer?
Yes, and that engineer's context usually accelerates the rollout considerably. We configure around whatever scope your own security function already owns.
07What does application security work cost?
Pricing follows codebase size, language diversity and how many features need threat modelling, set during an initial baseline. An ongoing practice is typically priced per cycle rather than as a single fee.
08How is this different from a one-time penetration test?
This runs continuously against every release rather than testing a fixed scope once. The two are complementary, not substitutes for each other.