01
Manual Effort
Thorough security testing still depends on hours of repetitive, hands-on work — mapping surfaces, crafting test cases and re-checking the same classes of issues every release.
AI Security R&D
Find vulnerabilities. Faster.
We’re building an AI-driven vulnerability detection engine that reduces manual security-testing effort and helps startups and SMEs move from discovery to remediation faster.
- less manual testing time
- ≈60%
- less manual testing timeInternal R&D measurement
- stage AI pipeline
- 6
- stage AI pipelineSurface → fix
- Founders Hub member
- 2024
- Founders Hub memberMicrosoft for Startups
engine / pipeline
Conceptual visualization- Application / API / Code
- Security Analysis
- AI Vulnerability Engine
- Findings
- Risk
- Remediation
Member of Microsoft for Startups Founders Hub since February 2, 2024.
Key resultMeasured in R&D
Reduction in manual testing time
In our internal R&D security-testing workflows, the engine reduced manual testing time by approximately 60%. That time goes back to the work that needs human judgment: reviewing findings, validating impact and shipping fixes.
Manual baseline100%
With the engine≈40%
EcosystemMicrosoft for Startups
Built with the Microsoft for Startups ecosystem.
bitRoot was accepted into Microsoft for Startups Founders Hub on February 2, 2024. Through the program we receive technology benefits, including access to Microsoft Azure cloud infrastructure and OpenAI models, which support our R&D work.
Milestone
Accepted into Microsoft for Startups Founders Hub
February 2, 2024
Announcement · February 2, 2024
bitRoot
Microsoft
for Startups
Founders
Hub
Member since February 2, 2024
- Microsoft for StartupsProgram participation and startup technology benefits.
- Microsoft AzureCompute and cloud services used across our R&D environment.
- OpenAIAccess to OpenAI models for our AI research and experimentation.
- February 2, 202401
Accepted into Founders Hub
bitRoot joins Microsoft for Startups.
- Cloud02
Microsoft Azure
Infrastructure for our R&D environment.
- Models03
OpenAI model access
Powers AI research and experimentation.
- Today04
R&D active
Building and validating the engine.
Program participation and technology benefits only. This does not imply endorsement, investment or partnership by Microsoft.
01The problem
Security testing shouldn’t become the bottleneck to shipping.
Three forces slow small teams down
- 02
Limited Security Resources
Startups and SMEs rarely have a dedicated security team. Testing competes with product work for the same few engineers, so coverage becomes a trade-off.
- 03
Slow Remediation
The longer the gap between a change shipping and an issue being found, the harder and costlier it is to understand the root cause and fix it.
02The engine
An AI engine built for vulnerability discovery.
A single workflow from surface to fix. Each stage is designed to remove manual effort while keeping security engineers in control of the decisions.
Stage 03 / 06
Detect — AI-assisted vulnerability discovery.
The AI engine reasons over the collected signals to surface candidate vulnerabilities, taking over work that would otherwise be manual test-case effort.
- Model-driven reasoning over signals
- Candidate vulnerability generation
- Less repetitive manual testing
Detect
03Architecture
From application to remediation, in one flow.
A conceptual view of the system we're engineering. Components and boundaries will evolve as R&D progresses.
fig. 01 — system architectureConceptual · subject to change
- 01
Applications
- Web applications
- APIs
- Source code
- 02
Security Analysis
- Surface mapping
- Signal collection
- 03Core
AI Vulnerability Engine
- Model reasoning
- Detection pipeline
- Context assembly
- 04
Security Findings
- Evidence & context
- Risk ordering
- 05
Remediation
- Fix guidance
- Re-test
- R&D infrastructure — Microsoft Azure · OpenAI model accessvia Microsoft for Startups
04What we're building
One engine. The whole security loop.
An AI vulnerability engine that connects your attack surface to the people who fix it.
fig. 02 — ecosystem mind map Signal Finding
AI engine
Attack surfaceInputs
- Web apps
- APIs
- Source code
- Dependencies
Cloud & AIPowered by
- Microsoft Azure
- OpenAI models
OutcomesOutputs
- Findings
- Risk ranking
- Context
- Fix guidance
Your teamsHuman in the loop
- Engineering
- Security
Map the surface01
Every app, API and code path in scope.
Reason with AI02
Models connect signals into candidate vulnerabilities.
Rank by risk03
Small teams fix what matters first.
Fix with context04
Where it lives, why it matters, how to patch it.
Re-test, continuously05
Every fix feeds the next run.
R&D ActiveAI SecurityVulnerability Detection
05Research & development
We’re still building.
Being in R&D means we can still shape the engine around the problems real teams have. We’re deliberate about what we claim, we measure what we build, and we’re looking to learn from engineering and security teams along the way.
- 01
Research
Study how vulnerabilities are found today and where AI can meaningfully help.
- 02
Experiment
Prototype approaches and test them against real security-testing workflows.
- 03
Engineer
Turn what works into a reliable, maintainable engine.
- 04
Validate
In progressMeasure results honestly — like the ~60% reduction in manual testing time we've seen in R&D.
- 05
Productize
Shape it into a product startups and SMEs can actually adopt.
Milestone
Accepted into Microsoft for Startups Founders Hub
06Who it's for
Built for teams that ship.
- 01
Startups
Ship quickly without leaving security testing until the end — or skipping it.
- 02
SMEs
Get more security coverage without needing to scale a large in-house team.
- 03
Engineering Teams
Find issues closer to when code changes, with context that makes fixing faster.
- 04
Security Teams
Spend less time on repetitive testing and more on judgment, validation and depth.
07Research & insights
Notes from the lab.
We'll publish research, technical write-ups and R&D notes here as our work matures.
- 01AI + CybersecurityComing soon
Where AI helps security testing — and where it doesn't
Notes on applying language models to offensive-security workflows.
- 02Vulnerability ResearchComing soon
Patterns behind common web & API vulnerabilities
Write-ups on vulnerability classes and how they surface.
- 03Security AutomationComing soon
Automating the repetitive parts of testing
What can be automated safely, and what still needs a human.
- 04R&D NotesComing soon
Building the engine in the open
Progress notes, decisions and lessons from our R&D.
ContactLet’s talk
Let’s build the future of security testing.
Whether you’re an engineering team that wants earlier feedback, a security professional, or interested in our research — we’d like to hear from you.