In this blog post Claude Code Security vs Traditional SAST Tools I Tested Both we will look at what happens when you put an AI-driven security reviewer next to classic SAST in a real engineering workflow.

The phrase “AI security scanning” gets people excited, and sometimes nervous. After testing Claude Code Security against traditional SAST tools in the same codebase, my main takeaway was simple.

Traditional SAST is still the best baseline for consistent, repeatable detection. But Claude Code Security is the first thing I’ve used that feels like a capable security engineer sitting with the team, reading the code with context, and proposing fixes that are actually usable.

High-level explanation before we get technical

Most organisations already have some form of SAST in CI/CD. It’s a sensible control: scan code before it ships, find common vulnerability patterns early, and create a measurable workflow around remediation.

What’s changed in the last 12–18 months is that code review itself has become “conversational”. LLMs can read a repository like a human does: across files, across layers, and sometimes across intent. Claude Code Security leans into that strength.

So the right question isn’t “Which one wins?” It’s: Where does each approach earn its keep, and how do you combine them without creating risk, noise, or theatre?

The main technology behind both approaches

What traditional SAST is really doing

Traditional SAST (Static Application Security Testing) analyses code without running it. Depending on the tool and language, it can range from rule-based pattern matching to deeper semantic and data-flow analysis.

In practice, most enterprise SAST programs rely on three mechanics:

• Rule sets for known bad patterns (dangerous functions, insecure API usage, risky configuration).
• Data-flow tracking (taint analysis) to see whether untrusted input can reach a sensitive sink (SQL, shell execution, templating, deserialisation).
• Policy and reporting so leaders can measure coverage, severity trends, and compliance gates.

The strength is consistency. The weakness is that codebases aren’t consistent, and neither are humans. Real vulnerabilities often hide in “business logic glue” between layers, where generic rules struggle.

What Claude Code Security is doing differently

Claude Code Security is closer to “security reasoning” than “static scanning” in the classic sense. The model reads the codebase with context, follows flows across files, and explains why something is risky in plain language.

Two capabilities stood out in my testing:

• Contextual tracing across multiple components (controller → service → repository → external call), including non-obvious flows.
• Scan-to-fix workflow where the output isn’t just a finding, but a proposed patch that matches the code style and structure.

It also performs a form of self-checking on findings (think “try to disprove myself before I interrupt the team”). That matters because false positives are the quickest way to train engineers to ignore security tools.

What I tested and how I tested it

To keep this grounded, I tested both approaches on a real-world style application: a typical line-of-business API with authentication, role-based access, several integrations, and a mix of legacy and newer modules.

I ran:

• A traditional SAST tool integrated into pull requests (your usual CI gate workflow).
• Claude Code Security as an on-demand reviewer on the same branch, with the same changes.

I wasn’t trying to “make one look good”. I wanted to understand triage time, fix quality, and whether the results changed developer behaviour. Those are the outcomes technology leaders actually feel.

What I found in practice

1) The biggest difference wasn’t detection, it was triage speed

Traditional SAST did what it normally does: it found a predictable set of issues, and it also produced a predictable amount of noise. Nothing shocking there.

Claude Code Security’s advantage was that it often answered the two questions every developer asks within 30 seconds of seeing a finding:

• Is this real in our context?
• What’s the simplest safe fix that won’t break the release?

That “time to clarity” is a big deal. In my experience, most AppSec friction comes from uncertainty, not from the fix itself.

2) Traditional SAST still wins on repeatability and governance

If you’re running an enterprise program, you need controls you can standardise and measure. SAST tools plug neatly into CI/CD gates, dashboards, and audit narratives.

Claude Code Security felt more like a highly capable reviewer than a compliance instrument. That’s not a criticism. It’s just a different tool shape.

For leaders, the implication is: keep SAST as the floor, and use Claude to lift the ceiling.

3) Claude was better at “logic-shaped” vulnerabilities

The most valuable findings weren’t the obvious injections everyone trains for. They were issues shaped like business logic.

Examples of what Claude handled well:

• Authorisation gaps where a check existed, but not at the right layer, or not for all paths.
• Trust boundary mistakes where “internal” data was assumed safe after being transformed.
• Multi-step exploit chains where each individual step looked acceptable in isolation.

Traditional SAST can catch some of this, especially with good modelling. But in many organisations, modelling lags reality. The code moves faster than the rule set.

4) The fix suggestions were good, but you must treat them like a junior PR

The patches Claude suggested were often impressively aligned with the code style. That matters because developers are more likely to accept changes that feel native to the codebase.

But I wouldn’t auto-merge any AI-generated patch, especially around auth, crypto, or input validation. A “safe-looking” fix can introduce subtle regression risk.

My rule: treat suggested patches as a strong draft. Review like you would review a junior engineer’s PR in a sensitive area.

5) The new risk isn’t code quality, it’s data exposure

This is where tech leaders need to slow the team down (briefly) and make a decision. AI security tools are powerful precisely because they read broadly across repositories.

That creates a governance question: what is the tool allowed to see and send?

In Australian environments, this lands quickly in real constraints:

• ASD Essential Eight expectations around controlling administrative privileges, patching, and hardening can be undermined if secrets leak via tooling.
• Privacy obligations if source code or configuration contains personal information, identifiers, or regulated data flows.
• Third-party risk if vendor tooling becomes part of your secure development chain.

So even though Claude Code runs locally as a client, you still need to think about what content gets transmitted for analysis, how retention works in your plan type, and how you handle secrets in repos.

A real-world scenario I keep seeing

An organisation modernises a customer portal. The team moves fast, uses a mix of APIs and serverless components, and relies on identity claims from an upstream system.

Traditional SAST flags the usual suspects: a few risky string operations, a couple of missing headers, and one or two potential injection paths that turn out not to be reachable. The security backlog grows, the team gets numb, and leaders see “lots of findings” but not “less risk”.

Claude Code Security, on the other hand, spots a subtle authorisation inconsistency: one endpoint checks role membership in the UI layer, but the service layer is callable through another integration path that bypasses the UI entirely.

That’s the kind of issue that survives for months because it’s nobody’s single bug. It’s a system behaviour problem.

Practical steps to use both without chaos

1) Keep SAST as the gating control

Use SAST to enforce non-negotiables in CI/CD. Leaders need an objective baseline, and engineers need predictable rules.

2) Use Claude Code Security for high-severity review and remediation acceleration

Run Claude on:

• Auth and authorisation changes
• New integrations and data ingestion paths
• High-risk modules (payments, PII handling, admin functions)

3) Standardise how findings become work

One anti-pattern I’ve seen: AI finds issues, people paste screenshots into chat, nothing becomes trackable work.

Decide upfront:

• What counts as a “must-fix now” vs “fix soon”
• How you attach evidence and reasoning
• How you measure remediation time without creating perverse incentives

4) Put guardrails around secrets and sensitive files

Before you scale usage, harden the developer workflow:

• Move secrets out of repos (and out of .env files where possible) into a managed secret store.
• Add secret scanning and pre-commit hooks.
• Decide which repositories are allowed to be analysed with AI tooling and under what account type/policy.

5) Teach the team what “good” looks like

AI tools can make teams faster, but they can also make teams sloppy if people outsource thinking. I’ve had the best results when we used Claude’s output as a teaching moment.

When a finding is real, turn it into a short internal pattern:

• What was the root cause?
• What was the secure pattern?
• How do we prevent it by default next time?

A small code example that shows the difference

Here’s a simplified pattern I used to compare how the tools reason about context. The code is intentionally small, but the behaviour is common.

// Example: indirect authorisation gap
// UI route checks role, but internal integration calls service directly.

function getCustomerReport(user, customerId) {
 // UI layer check
 if (!user.roles.includes("report_viewer")) {
 throw new Error("Forbidden");
 }
 return reportService.generate(customerId);
}

// Somewhere else...
function nightlyIntegrationJob(customerId) {
 // No user context, calls service directly
 return reportService.generate(customerId);
}

const reportService = {
 generate(customerId) {
 // Pulls sensitive fields
 return db.query("SELECT * FROM reports WHERE customer_id = ?", [customerId]);
 }
};

Classic SAST might focus on the query safety (and here it’s parameterised, so it looks fine). Claude’s strength is noticing the control problem: the authorisation check is placed where it can be bypassed by a legitimate internal call path.

This is why I see AI review as complementary. One is checking for known “bad shapes”. The other is questioning whether the system’s behaviour matches the intended security model.

My takeaway as an architect

After 20+ years working across enterprise architecture, Azure and Microsoft 365, and now AI-driven engineering workflows, I’m convinced we’re entering a period where security tooling will be judged less by how many findings it produces and more by how quickly it helps teams reduce real risk.

Traditional SAST remains essential because it gives you repeatability, governance, and a dependable baseline. Claude Code Security is valuable because it compresses the distance between “finding” and “fix”, and it’s unusually good at reasoning about context and intent.

The forward-looking question I’m sitting with is this: as AI reviewers get better at proposing patches, will we finally redesign secure development so that remediation is the default outcome, not an ever-growing backlog?