SAST in 2025: how to choose SonarQube, Semgrep or Checkmarx

What teams struggle with when adopting SAST

In 2025 teams don’t adopt SAST for the report — they want to find vulnerabilities and dangerous patterns earlier in code: injections, unsafe data handling, authorization mistakes, secret leaks and some bugs that later become incidents.

After the first run many teams start to ignore alerts. Not because “no one cares,” but because useful signals quickly get lost in noise.

Noise usually appears for understandable reasons: many false positives and cases where, without context, it’s unclear if there is real risk; reports arrive too late (after merge or release); no clear priority rules (what’s critical and what can wait); no process owners (who triages, who creates tickets, who approves exceptions); the tool immediately “blows up” on legacy code and brings thousands of issues on day one.

From a quality gate people usually expect two things at once: less risk and less noise. You need a clear stop on truly dangerous changes (for example, a new critical vulnerability in payment code or a service handling personal data), but without halting development because of old debt.

Here it’s important to separate tool choice and process configuration. The tool covers languages, rule accuracy and integration convenience. The process covers how the team lives with results: a baseline for old problems, fix timelines by severity, clear exceptions and the principle that the gate looks at new code. Without that, even a good SAST becomes background noise and eventually gets turned off.

SonarQube, Semgrep, Checkmarx: what to compare

Compare these tools only on the same tasks: how they find problems in your code, how trustworthy results are, and how comfortable daily checks are to live with.

SonarQube is often seen as “code quality plus security”: many rules about bugs, maintainability and style, with security alongside them. Semgrep is closer to “fast and flexible”: rules can be written and changed like code, which suits teams that want to control check logic. Checkmarx is usually chosen where a more "enterprise" contour is needed: a broad set of checks, policy management, reports and the process around vulnerabilities.

Another question is deployment. Cloud speeds up start and scaling, but may not fit strict data and network requirements. On‑prem gives more control but adds operations: infrastructure, updates, accounts and backups.

Fix your comparison criteria in advance. Common ones are:

• coverage (languages and frameworks used in your stack),
• accuracy (useful alerts versus "noise"),
• speed (how many minutes the check adds to CI),
• developer experience (IDE, comments in PR, clear fix guidance),
• manageability (roles, policies, audit, security reporting).

Don't forget hidden costs. In practice time is spent not on buying, but on tuning rules, integrating with CI and the issue tracker, maintenance and updates (especially on‑prem), training the team to triage findings and agreeing who accepts risk.

If you agree in advance what you compare and how you measure success, the pilot will be short and honest instead of a presentation contest.

Choosing by languages and stacks: how not to miss

Start not from product names, but from what actually lives in your repositories: languages and versions, frameworks, build, tests, typical patterns (ORMs, template engines, API gateways). A common mistake is picking a “top” product and later finding it works great for one service and is almost useless for half of your code.

If you have a monorepo and multiple teams, it’s important not only that a language is supported but also how the tool behaves on a large tree with different needs. A frontend team usually needs fast feedback in pull requests, while critical platform services need stricter checks and policies. Before the pilot, verify basics: can the tool scan changed parts only, apply different rules for folders or projects, produce component-level results (not just a single report) and run in your CI without constant manual workarounds.

Consider product specifics. For backend, injections, unsafe serialization, authorization bugs and secrets are often most important. For frontend — XSS, unsafe DOM handling and dependencies. For mobile — token storage, cryptography, network settings and build profiles. One tool may be strong in Java and weak in Swift (or vice versa).

Check support not by marketing tables but with a small test on your code. Take a real service or a mini-project and run a short pilot: 2–3 vulnerabilities you want to catch; 1–2 places with frequent false positives; run on branch and on pull request; then evaluate — did it find what you need and how much time did triage take.

Also think about IaC and configs (Terraform, Kubernetes YAML, CI scripts). These often give quick wins but add noise at the start. A practical compromise is to include IaC after you reach acceptable alert quality for main languages and begin with the most critical rules (open ports, public buckets, secrets in variables).

Rules, updates and exceptions: how to assess value before buying

The winner is not the tool with "more rules" but the one whose rules cover your real risks and don’t turn work into endless triage. Treat rules as a product: who writes them, how they update and how clear the results are.

A good rule usually relies on a clear source (CWE, OWASP, vendor secure coding guides or research), gives a confidence level and shows examples. Examples often matter more than a formal description: if an alert doesn’t explain what is dangerous and how to fix it, a developer either ignores it or guesses a fix.

To quickly evaluate a rule set in a pilot:

• pick 10–15 typical vulnerabilities for your languages (injections, SSRF, unsafe deserialization, secrets),
• see how the tool explains fixes and how consistently it maps to CWE/OWASP,
• assess signal/noise on real code,
• check how often rules are updated and what happens to thresholds after updates.

Custom rules aren’t needed everywhere. They are useful if you have internal frameworks, specific safe wrappers or regulatory coding patterns. But they almost always become a burden for 1–2 people. If those people don’t have time, rules will age and trust in SAST will drop.

Exceptions are acceptable when a risk is accepted consciously. Common practice is to record the reason and a review date: "false positive due to sanitizer", "code only for tests", "planned refactor in the next release". Better to tie an exception to a specific location and version rather than disabling a rule everywhere.

Alert quality: how to measure and improve

Alert quality is easier to measure not by the number of findings but by how many of them actually help. False positives and misses exist in every tool — those are working metrics.

For a pilot, three metrics are usually enough: false positive rate (what was closed as irrelevant), repeatability of alerts for known patterns and mean time to resolution for critical findings. Misses are useful too: if a bug was found in production or in a pentest, record whether SAST could have caught it and which rule would have applied.

To prevent alerts from becoming noise you need a simple triage process. It shouldn’t live in one person’s head. Minimal framework: developer confirms context, security or an architect helps prioritize and exceptions, then there are clear deadlines and statuses (fixed, false positive with reason, not applicable with justification, deferred with date).

Duplicates and recurring patterns consume most time. "Normalization" helps: group identical alerts into one task for the pattern, then decide whether it’s cheaper to fix the code template, tune the rule or add a targeted suppression.

Management doesn’t need pages of details. 3–5 metrics are enough: how many critical are open, how many closed in the period, average reaction time, top causes of recurrence and false positive rate (to see rule quality improvement rather than finger-pointing).

Step-by-step pilot plan: from first run to scale

Start the pilot not by connecting all repositories, but with a measurable experiment. Choose 1–2 projects that reflect your stack: one actively developed service and one typical by architecture (for example, backend plus some frontend). Set a goal for 2–4 weeks: reduce critical vulnerabilities, improve code quality or make releases more predictable.

Integrate analysis into CI so it’s visible but doesn’t break the process on day one. Often running on merge requests and nightly scans is enough. The team also needs a ritual: who looks at alerts, where they are discussed, how the decision to "fix", "suppress" or "rewrite a rule" is made.

After the first run do triage on 50–100 alerts and mark which rules cause most noise. Usually 5–10 rules create the bulk of false positives or don’t fit your patterns. In legacy projects this is noticeable: e.g., a rule about "method complexity" may flood the report with hundreds of notes and distract from security.

To keep the pilot from turning into an opinion battle, record the "rules of the game" as a pilot outcome: which categories block, which exceptions are allowed and who approves them, whether accepted risk is allowed temporarily and for how long, what thresholds define success, and what configuration template will be used in new repositories.

Once that is documented, scaling becomes copying the template rather than reimplementing. Onboard teams in waves and start each wave with a short review using examples from their code.

Quality gate without sabotage: thresholds, legacy and exceptions

Quality gate usually fails not because of rules but because of expectations. If you set the same threshold for new and legacy code, teams start fighting the tool instead of vulnerabilities. The workable approach is almost always the same: enforce strict checks for new code and reduce legacy debt step by step.

For new code the gate should be strict, but it’s better to start gently or you’ll block releases on day one. A good start is to catch what’s clearly dangerous and rarely disputed: high or critical issues with an obvious exploitation path, blocking patterns like SQL injection and auth bypass, secret leaks (keys, tokens) and, where supported, critical dependencies with known CVEs.

For legacy, set a baseline and reduce debt in increments. For example, each sprint close X most dangerous findings or reduce the number of high issues by Y percent. That way the gate doesn’t become a wall.

Exceptions should be part of the process, not a loophole. Practical minimum: exceptions are approved by an assigned security owner or tech lead, each exception has a term (often 30–90 days) and a reason, and after expiry the issue returns to work.

Developers accept a gate more easily when it brings benefits: fewer manual checks, faster reviews and fewer surprises late in the cycle. A typical compromise: allow merge if new code adds no high alerts or secrets, and keep legacy in a separate backlog.

Common mistakes in selection and adoption

Most failures come from expectations, not the tool. In 2025 almost any team can run a scanner in a day, but a stable process appears only if you agree in advance: what blocks a release, who triages alerts and how much time it actually takes.

Common repeated mistakes: enabling all rules at once and getting hundreds of alerts where important ones drown in minor issues; setting a strict gate on the entire legacy and stopping releases; not assigning a triage owner and accumulating duplicates; ignoring scan time and stretching CI so checks begin to be bypassed; trying to replace SAST with training (training helps, but without automated checks mistakes return under deadlines).

A simple antidote usually has four steps: start with a small rule set and expand as trust grows; set two thresholds (strict for new code and soft for legacy); assign a triage owner and SLA; measure scan time and decide what runs in PR and what goes to nightly jobs.

Quick pre-deployment checklist

To pick SAST and avoid breaking CI, run a few quick checks on one repository and one typical pipeline.

In one day you can usually understand the core:

• whether your languages and versions are supported in a real build,
• how long a scan takes and how it varies on heavy branches,
• whether rules can be configured at team or repo level,
• whether there is a clear triage (owners, false positive recording),
• whether new code can be separated from legacy and only new problems blocked.

Then separately test behavior on a "noisy" project. In a corporate monorepo it’s important that a developer can quickly see: is this a real vulnerability or a debatable rule, and what exactly to change in code.

Before enabling the gate everywhere, record answers to several questions: which reports and metrics security, development and management need; how baseline and "new code" are counted; how exceptions work (term, reason, approver); how rule updates affect thresholds and stability.

Example: how a team reached a working gate in a month

A product support team had a monorepo with three services: Java (main backend), JavaScript (admin) and Python (data processing package). Releases went out weekly and any delay hit the schedule.

In the pilot they enabled a basic rule set and scanned the whole repo. The result was demotivating: about 1,200 alerts, most about style and debatable recommendations rather than real risk. Reports were quickly ignored.

They treated it like noisy monitoring: reduce noise first, then enable blocks.

Their 4‑week plan looked like this:

• Week 1: mapped the baseline (how many alerts, hotspots, what gets confirmed). The pilot goal was not "fix everything" but make the gate useful.
• Week 2: turned off several rules that caused most false positives. For legitimate exceptions they added comments with reason and review date.
• Week 3: started a weekly 30–40 minute triage and agreed on statuses.
• Week 4: configured the quality gate to block only new high-severity alerts. Legacy issues moved to a separate backlog with owners and due dates.

After a month reports shortened, releases stopped being blocked by historical debt, and developers began trusting signals: the gate rarely blocked merges, but when it did it was almost always for a real issue.

Next steps: from comparison to a working process

The biggest risk after choosing a tool is stopping at selection and not forming a habit. The winning SAST is the one that becomes part of everyday development without killing velocity.

Before scaling, do a short preparation: inventory languages and versions, key repositories and CI; appoint process owners (security for policy and risk, team leads for team acceptance, DevOps for CI and access, management for priorities and timelines); a minimal quality gate only for new code; a clear exception process; a 2–4 week pilot on 1–2 repositories.

To make the pilot measurable, pick 3–5 metrics and record them weekly: false positive rate, average time to triage, how many findings were fixed, how often builds were blocked and why.

A "quiet mode" in the first week works well: results are visible but builds don’t fail. On week two enable blocking for a single class of problems (for example, secrets and SQL injection), and keep style and code smell rules as recommendations.

If you lack hands to configure CI, runners, store reports and handle exceptions, this becomes a DevSecOps and integration task. In such cases it’s often easier to involve a system integrator. For example, GSE.kz (gse.kz) does system integration and 24/7 support and can help set up SAST adoption and quality gate rules so they reduce risk instead of causing conflicts.