Two products on the same codebase. Risk management keeps one register for every service you run, scored on where each flaw lives. Security audit reviews every change with a pipeline of agents, then verifies each finding before it reaches you. What the audit finds, the register keeps.
Every finding consolidated into one register and scored on what the flaw is, where it runs, and which service it sits in.
A pipeline of agents maps the code and proposes findings, then puts each one through a verification stage that confirms it or drops it.
Most scanners give you a list and stop there. The list grows, the same issue comes back under a new line number on every run, and severity gets assigned as if every repository mattered the same.
SafeStackAI splits the job in two. The audit decides what is real. The register decides what matters, and remembers the decision.
Every finding lands in one risk register, scored on what the flaw is, where it runs, and which service it sits in. The same flaw counts for more in a production payments service than in an internal tool.
A critical finding in something nobody can reach is not your biggest problem. Every risk is scored on five factors, and two of them come from the service it belongs to. Group your repositories into services, set how critical each one is, and the ranking follows.
As the analysis reports it.
A 0–10 assessment, floored at 0.3. Hard to exploit is still a finding.
How much the service matters. You set this.
Code not reachable from production is a smaller problem.
A decision you already made is not an outstanding risk.
A repository on its own tells you nothing about blast radius. Group repositories into services, set criticality and environment once, and every risk underneath gets scored against real business context.
Findings are fingerprinted, so a risk keeps its identity when the line numbers around it shift. Dismiss something once and it stays dismissed. If a fixed risk comes back, the register marks it regressed instead of filing it as new.
Findings from more than one engine land in the same register, under the same severity model, ranked against each other.
Hundreds of vulnerability checks across six languages, run with full repository context and verified before they reach the register.
Connect your existing projects and their findings become risks in the same register, scored against the service they belong to.
Reviews run on the diff and post back to the pull request, and each review shows which existing issues the change resolved.
Rules your team writes, applied to every risk in the register, whichever engine found it.
A manual audit is thorough and it happens once. The next pull request lands against a report that is already out of date. This runs the same kind of review on every change, and it is a pipeline of agents rather than one pass with a pattern list.
Each stage does one job, and nothing reaches the report without passing verification.
The code is mapped first: call flow, entry points, and a threat model of what an attacker would go after.
Two audit passes read the code in parallel while static analysis runs against it at the same time.
The same defect reached through several paths is folded into one finding before anything is judged.
Every remaining finding is confirmed or dropped. Anything the verifier cannot settle is kept for your review.
What survives is written out the way an audit report is, with severity, the exact lines and a recommended fix.
Pull request review runs its own pipeline, planning checks for each changed file and handing them to specialist agents.
Every result comes back with the reasoning behind it. Severity, the category it falls under, the exact lines, what is wrong and how to fix it.
Agents map call flow, entry points and a threat model before they look for flaws, so each finding is judged against how the code is actually reached.
Every proposed finding goes through a verification stage that confirms it or drops it. Anything it cannot settle is kept for your review rather than silently discarded.
The same defect reached through several paths folds into one finding with a count, so a repository with one bad pattern does not read as twenty separate problems.
Each run is written up the way an audit report is, not dumped as a list. The PDF carries the commit, how many files were analysed, and the findings critical first.
Dismissing a finding takes a reason and a written justification, so the next person to read it knows why it was closed.
An audit cannot be marked reviewed while any of its findings is still waiting on a decision.
Open a session on any analysis and question the findings against the code that produced them. The assistant reads the repository at the analysed commit and points to the file and line behind each answer, so you can check it instead of trusting it.
A session streams its answer as it works through the code. When there is a fix, it comes as a diff you can copy straight into the branch.
Smart contract audits follow execution instead of matching patterns.
Every entry point traced as an execution tree and as a call graph.
High-value Slither detectors run next to the agents on every Solidity audit.
Ranked by severity, each carrying a confidence score.
Scored contract by contract against the standards it implements.
Install the app, connect your repositories and switch on automatic review. There is no workflow file to add, no CI step to maintain, no scan to schedule and nobody to remember to run one. Both products run from that one install.
Install the GitHub App, connect the repositories you want watched and turn on automatic review. Nothing goes into your CI.
Opened, reopened, or pushed to again, the diff gets reviewed with the repository's context behind it and the findings post back to the pull request.
A merge to the default branch starts a complete audit of the repository, and every finding is fingerprinted against what the register already holds.
New risks are added, returning ones matched to their history, scores move with them. The register reflects the last commit rather than the last audit.
Smart contracts and the services around them, reviewed by the same agents and held to the same verification.
Everything you need to audit one codebase on every change.
Several services under continuous review, with a team working in them.
Custom plans tailored to your needs.