AWS CodeGuru

Describe AWS CodeGuru by the failure it prevents, not by its feature list. It belongs to AI-assisted code review and profiling and runs at the code stage of delivery, which matters because the cost of fixing a defect rises sharply the later it is found — the core argument behind shifting security left.

Strong answers cover Reviewer for pull-request comments and security detectors vs Profiler for runtime CPU/heap hotspots, repository association, how it compares with dedicated SAST, and the migration toward Amazon Q Developer reviews. Interviewers also listen for the operational side: who owns the rules, how findings reach the developer who introduced them, how false positives get suppressed without silently disabling coverage, and what the break-glass path is when a fix genuinely cannot ship in time.

Tie it to the wider toolchain. Security scanning is only useful when its output is actionable, deduplicated across tools, and attached to a specific commit, image digest or resource — otherwise teams learn to ignore it.

The usual failure mode is a security gate so slow or noisy that teams bypass it. For AWS CodeGuru, the practical levers are: scan only what changed on pull requests and keep the exhaustive scan for a nightly or pre-release run; cache whatever database or ruleset the tool downloads so CI is not re-fetching it every job; and parallelise across services rather than scanning a monorepo serially.

Severity policy matters as much as speed. Blocking on every finding trains people to add blanket suppressions. A workable policy blocks on Critical (and often High) with a documented, time-boxed exception process, while everything lower is tracked as a ticket rather than a hard stop.

For this tool specifically, tune Reviewer for pull-request comments and security detectors vs Profiler for runtime CPU/heap hotspots, repository association, how it compares with dedicated SAST, and the migration toward Amazon Q Developer reviews. Finally, make the gate honest: a pull-request review comment blocking merge on a detected security issue. A gate that warns but never blocks is documentation, not a control.

Every scanner produces findings that are not real risks in context — an unreachable code path, a vulnerable function the app never calls, a test fixture that looks like a credential. Answer this by describing a triage flow rather than claiming the tool is accurate.

First, separate "is this a true finding" from "is it exploitable here". Reachability, network exposure, and whether the affected component handles untrusted input all matter more than the CVSS number alone. Second, use the tool's own suppression path for AWS CodeGuru rather than deleting the check — Reviewer for pull-request comments and security detectors vs Profiler for runtime CPU/heap hotspots is usually where this is configured. Every suppression should carry an owner, a justification and an expiry date, and should be reviewed like code.

Third, baseline. Adopting a scanner on a mature codebase produces a wall of findings; freezing that backlog and blocking only on newly introduced issues is what makes adoption survivable. Then burn the backlog down deliberately.

Start with verification: a Critical rating is a claim, not a fact. Confirm the affected version or resource is actually deployed and that the vulnerable path is reachable from untrusted input.

Then contain according to the finding class. A leaked credential is rotated first and only then removed from history — deleting the commit does nothing if the key is already public. A vulnerable dependency is patched or the affected feature disabled. A compromised host is isolated for forensics before termination, since terminating destroys the evidence. A misconfigured policy is corrected and audited for other resources sharing it.

Close the loop in the pipeline. The point of DevSecOps is that the same class of issue should be caught earlier next time: add or tighten the rule, lower the threshold, or add a policy at admission. For AWS CodeGuru, that usually means a pull-request review comment blocking merge on a detected security issue.

Finish with the blameless review — how long the exposure lasted, why the existing gates missed it, and what detection you added.

Interviewers use "compare X to Y" to check you understand tradeoffs rather than tool names. For AWS CodeGuru, the honest comparison is dedicated SAST (Semgrep, CodeQL) — CodeGuru adds AI review comments and runtime profiling, but is narrower than a full SAST engine and is being folded into Amazon Q Developer.

Choose on concrete axes: what stage it runs at (code), how it integrates with your existing CI/CD and registries, how noisy it is out of the box, whether it is open-source or a paid managed service, and whether it covers the platforms you actually run. Coverage overlap matters too — running two scanners of the same class mostly multiplies triage work unless one meaningfully catches what the other misses.

Strong answers avoid "X is just better." They say when each wins: pick the tool whose stage, ecosystem fit and signal-to-noise match your constraints, and note where you would run something else alongside it.

Operating a scanner is not "turn it on" — it needs the same observability as any production system. For AWS CodeGuru, the signals that matter are recommendations accepted vs dismissed on pull requests, and — for Profiler — CPU/latency hotspots and estimated cost of the most expensive methods.

Trend them. Rising Critical counts on a stable codebase usually means a base image or dependency has aged, not that developers got careless. A rising false-positive rate is an early warning that the gate is about to lose the team's trust. Scan duration matters because a slow gate is a gate people route around.

Feed these into a dashboard alongside remediation SLAs so security posture is visible to the people who own it, not buried in a tool nobody logs into.

Reviewer analyses pull requests and posts recommendations — including security detectors for issues like hardcoded secrets and injection — as inline comments, so review feedback is automated. Profiler runs in production, samples CPU and memory, and pinpoints the specific methods burning the most time or money, which is optimisation guidance you cannot get from static analysis. AWS is consolidating Reviewer's capabilities into Amazon Q Developer's code reviews.

Do use Profiler to find real runtime hotspots before optimising; do not treat CodeGuru Reviewer as a complete SAST replacement, since a dedicated engine has broader rule coverage.

The anti-pattern usually comes from treating the tool as a checkbox rather than a control people have to live with. A gate that is too loud gets bypassed; a gate that blocks on things nobody can fix gets disabled; a suppression with no expiry becomes a permanent blind spot. The best practice above is the version that survives contact with a real team under delivery pressure.

Generally: make the signal trustworthy (low noise), make the required action clear and achievable, give an honest escape hatch for genuine exceptions, and review suppressions and thresholds on a schedule so the configuration does not quietly rot into "always green, never useful."