Understand public exposure
Understand what your website and domain reveal publicly, including email authentication and visible configuration. We explain which observations matter and which require further testing.
FRONTIER AI–POWERED SECURITY & PROTECTION
Frontier AI.
Sharper security.
Stronger protection.
We’re building security services that combine frontier AI models with human expertise to help protect your websites, customer portals and APIs. Understand your exposure, validate suspected weaknesses and turn evidence into practical fixes.
Pilot services in development · Authorized testing · Human-reviewed findings
CHEROBITFrontier intelligence. Human oversight.01 Your website
02 Customer access
03 Connected APIs
[ ANALYZE ] → [ VALIDATE ] → [ PROTECT ]
Concept illustration · no live monitoring
FRONTIER AI ANALYSISAUTHORIZED TESTINGHUMAN VALIDATIONVERIFIED CORRECTIONS
INTELLIGENCE WITH ACCOUNTABILITY
Advanced models.
Grounded decisions.
AI can help investigate a risk. Evidence determines whether it is real.
Our developing workflow uses frontier models to analyze scoped observations, explore possible weaknesses and recommend corrections. Human review checks every conclusion against the evidence before it becomes a finding.
Saved evidence→AI analysis→Human validation
Capabilities depend on the model and approved access available. Lab demonstrations are labeled; we do not claim unrestricted access, autonomous protection or vendor affiliation.
01 / THE APPROACH
Security questions.
Useful answers.
Choose a focused review around the systems you rely on. We agree the scope first, explain what the evidence supports and give your team a concrete next step.
PUBLIC INFORMATION
Validate a defined risk
With the appropriate owner’s permission, review whether users can access only their own records and whether agreed business rules hold. Use dedicated accounts and synthetic data within a defined scope.
Explain and verify the fix
Receive a plain-language summary for your business and supporting details for your developer. For confirmed issues, document the correction and retest the same scenario after the fix.
02 / SHOW THE WORK
A finding should
come with proof.
A good review reports working protections as clearly as confirmed issues. Explore this local lab example to see how observations become an honest conclusion.
Local Fluent Forms review · November 2026
Synthetic accounts and records. No customer data.
LOCAL LAB EXAMPLE● CONTROL HELD
The access boundary held.
Six private configuration routes rejected visitors and ordinary subscribers. Administrator controls succeeded.
Anonymous 401 · Subscriber 403 · Administrator 200
WHAT YOU TAKE AWAY
A report your team
can act on.
Understand the impact.
What was tested, what was confirmed and what it could mean for your operations.
Know the next step.
Traceable observations, reproduction details within scope and recommended corrections.
Check the outcome.
A follow-up check of the agreed scenario, with remaining limits stated clearly.
03 / HOW WE WORK
Clear boundaries.
Honest conclusions.
A useful security service earns trust through the quality of its evidence.
Permission comes first.
Active testing requires a defined scope and approval from the appropriate system owner.
Your data stays out of the demonstration.
We prefer synthetic records and dedicated test accounts. Secrets and personal information do not belong in public reports.
Frontier AI, with human accountability.
Read our review standards ↗Frontier models support evidence analysis and remediation planning within available access. Human review validates findings; model output alone is never proof of a vulnerability.
Enterprise AI experience.
A practical focus on security.
Yazdan is an AI engineering leader whose experience spans financial services, cloud platforms and production machine learning. He currently serves as Sr. Executive Director, Voice and Agentic AI at JPMorgan Chase.
His background includes leading the Financial Services AI Lab at Capital One and engineering leadership roles at Amazon Alexa AI and AWS. His work spans generative AI, multi-agent systems, AI governance and production ML platforms.
At Cherobit, he is applying that experience to the development of evidence-backed security reviews that combine frontier AI analysis, scoped testing and human validation.
Company names describe professional experience. Cherobit is an independent venture; no employer endorsement, sponsorship or partnership is implied.LET’S DEFINE THE RIGHT REVIEW
Let’s start with
one clear question.
Tell us about your website or customer workflow and the risk you want to understand. We’ll discuss a focused pilot, its scope and the expected deliverables.
Start a conversation ↗
cherobit.lab@gmail.com
This opens your email app. Please send a brief description, without passwords, API keys, or customer records.