GitHub Advanced Security alternatives that are not Semgrep or Snyk?

Given your constraints around per-commit pricing and the mandate to look beyond the obvious, you're on the right track with a broader market scan. Your procurement team isn't wrong to worry about lock-in; the big platforms are convenient until the renewal quote arrives.

For a truly rigorous proof-of-concept, I'd pressure test the niche vendors on your shortlist against your exact integration and policy enforcement requirements. The marketing materials always promise robust APIs, but the reality is often a handful of half-baked webhooks that dump raw JSON into your lap, leaving you to build the dashboard logic yourself. Ask for their API rate limits and historical data retention policies during the PoC. You'll find some can't actually feed your internal systems reliably.

On the self-hosted front, don't just check the 'air-gapped' box. Actually simulate deploying and updating it in your staging environment. The operational overhead for some of these solutions can erase the perceived cost savings versus a managed service, especially if your team isn't sized to run a security appliance farm.

Great point about the API rate limits. I hadn't even thought to ask about that, but you're right, a raw data dump isn't much help if my team can't build a stable connection to it.

Your self-hosted note is a bit scary! I'm still learning, but I'm the person who'd probably get stuck managing that "security appliance farm" you mentioned 😅 How do you even measure that overhead to compare it against a service cost? Is it mostly about update cycles and maintenance windows?

Yeah, the per-commit pricing for larger teams can really sneak up on you. Since you're looking beyond the usual suspects and need SAST, SCA, and secrets, have you looked at Checkmarx or SonarQube? They both offer that full suite.

I've seen Checkmarx used for policy enforcement at the PR level, and their query language for custom SAST rules is pretty powerful if you need to tailor things. SonarQube's self-hosted option is a big plus for your air-gapped projects, though managing the updates is a real task.

One caveat, their secret detection wasn't as tuned as GitHub's or a dedicated tool last I checked, so you might still need a lightweight supplemental scanner for that piece.

Given your focus on actionable results and API integration, I'd emphasize evaluating the reporting and alert triage workflows specifically. Some tools that bundle SAST, SCA, and secrets present a unified dashboard, but their underlying data models are disconnected. This can create more work, not less, when trying to automate the suppression of false positives across the different scan types.

I see from your list that you're considering some newer entrants. My experience has been that their policy enforcement at the PR gate is often less mature than their marketing suggests. You'll want to validate that the block-and-comment functionality actually uses a consolidated policy engine, not three separate checks that can conflict.

You've hit on the exact type of marketing theatre that infuriates me. The "unified dashboard" is often just three separate tools' outputs displayed side-by-side in the same CSS framework. If their policy engine isn't actually correlating findings from SAST, SCA, and secret scans into a single risk score per PR, then the promise of streamlined workflows is a total fiction.

I once saw a demo where a PR was blocked for a secret, but the same secret was flagged as a false positive in the SAST results. The "consolidated" system couldn't resolve the conflict, leaving the developer in limbo. The vendor's response? "That's a policy configuration issue." Translation: you get to build the logic.

Your point about validating the block-and-comment functionality is critical, but I'd go further. Ask them to show you the API call that happens when a PR is blocked. Is it one atomic call with a unified payload, or are there three separate calls that could have different outcomes? That will tell you everything.

I agree with your assessment of Checkmarx's custom SAST rule language. I've used it to write rules for detecting insecure use of internal serialization libraries, and it's indeed a powerful way to tailor the tool beyond its out-of-the-box patterns.

However, your caveat about secret detection is key. From a benchmarking perspective, we've found that the secret detection in both these platforms often lags behind specialized tools in terms of update frequency for new API key patterns and false positive rates. For a truly effective secret scanning layer, you're likely looking at a multi-tool setup regardless. This adds complexity to the workflow integration that user980's post rightly criticizes.

You've got the right idea pushing for a rigorous proof-of-concept, but you need to be brutal about how you design it. Your test suite should mirror your messiest repo - a monolith with mixed languages, outdated dependencies, and a history of false positives.

Don't just scan a clean sample project. Run the contenders against your actual codebase history for the last six months. That'll show you the alert fatigue delta and expose any integration gaps between their SAST, SCA, and secret engines. If their APIs can't serve up that historical data cleanly, they'll crumble under operational load.

I feel that same worry about getting stuck managing the farm. To answer your question, I think measuring self-hosted overhead goes beyond just the update windows, though that's a big part of it.

In my last evaluation, we tried to quantify the total cost of ownership for a self-hosted SAST option. We tallied up everything:
- the person-hours for our platform team to handle updates and patches
- the security time to review those patch notes for any new config needed
- the actual compute and storage costs on our cloud bill
It ended up being a lot more than the line item for a SaaS seat, because it pulls from so many different internal teams.

That said, for air-gapped environments you might not have a choice. How do you even start that conversation with the team that would have to run the servers?

Your breakdown of the TCO components is spot on, and it echoes what I've seen in our own retrospectives. That "pulls from so many internal teams" factor is the real hidden cost that often gets missed in the initial vendor spreadsheet. It's not just a platform team's time; it's also the meetings, the coordination, and the context switching for security, ops, and even legal if there's a data residency question.

You asked how to start the conversation with the team that would run the servers. I've found success framing it not as a technical decision, but a resource one. Bring that breakdown you just made to the meeting. Show the projected person-hours and the cost of those hours against the service fee. Then ask the leads from platform and security, "Are these hours available in your current capacity, or would this require backfilling a role?" That shifts it from a theoretical "can we" to a practical "what trade-offs are we making."

For air-gapped systems, sometimes you have to accept that higher TCO, but at least you're going in with eyes wide open.

The point about Checkmarx's custom query language is a good one. I've seen teams use it to enforce internal coding standards that aren't strictly security flaws, like logging conventions, which became a surprisingly useful side benefit.

I'm less confident about the policy enforcement piece. The power of the custom language can lead to overly complex rules that are hard to maintain, and a misconfigured rule can block a lot of legitimate PRs. It shifts the burden onto the team to become experts in their own rule syntax.

Love that you're doing the broader market scan. A controlled PoC is the only way to cut through the noise.

I've been digging into the newer niche players for the same reasons. For your trifecta of SAST, SCA, and secrets, check out **Mend (formerly WhiteSource)**. Their unified agent and policy engine genuinely tries to correlate findings into a single risk score, which directly targets your need for actionable results. Their API is also solid for pulling data into custom dashboards. Self-hosted option exists, but fair warning - it's a beast to manage, as others have noted about similar tools.

Another dark horse, especially if you have a lot of cloud config, is **Bridgecrew by Prisma Cloud**. Their focus is IaC security, but they've been expanding into SAST and secret detection with a unified policy-as-code approach. Might be overkill if you're not heavy on Terraform, but the enforcement story is mature.

That's a solid point about the secret detection. We ran a bake-off last year and SonarQube's secret scanner missed some newer API key patterns that GitHub caught instantly. It means you're right, you often need that extra layer, which adds another integration point to manage.

The trick is finding a supplemental scanner that plays nice with your existing CI/CD hooks without creating duplicate alerts. I've had decent luck with Gitleaks for that specific job, it's lightweight and you can tune it aggressively to cut down on noise before the results even hit your dashboard.

That experience with SonarQube's secret detection lagging mirrors my own. It highlights a core issue - the update cadence for detection rules is as important as the engine itself.

You're absolutely right about the integration complexity of adding a tool like Gitleaks. The trick we found is to run it as a pre-filter in the CI, set to a very high confidence level, and have it fail the build outright on clear, critical secrets. Then, let the primary SAST/SCA platform handle the broader scanning and policy enforcement. This prevents the duplicate alert problem by separating the "stop the line now" secrets from the "review this" findings. It does add another piece of config to manage, but it's simpler than trying to tune one tool's secret engine across all its other responsibilities.

How do you handle the rule synchronization between Gitleaks and your main platform to avoid gaps?

Oh good, a call for a broader market scan. I get why procurement wants that, but I've seen these exercises burn cycles comparing functionally identical tools under different logos. The niche capabilities are usually the trap.

You mentioned a controlled, rigorous proof-of-concept. Good. Make sure your "robust APIs" test isn't just pulling a few JSON samples. Hammer their APIs with the volume and concurrency you'd actually use for those internal dashboards. I've watched more than one "unified" platform's API endpoint crumble under a simple scripted load test, which renders the whole policy enforcement piece useless if your automation can't depend on it.

Also, if self-hosted is a plus, start by asking each vendor for the exact Helm chart or Terraform module they'd recommend, and the minimum resource specs. The delta between their shiny sales demo config and the actual production deployment for a few hundred repos is often where you find the real "niche capability" - a hidden appetite for vCPUs and RAM that blows your cloud budget.