Intelligent ASPM
Stop shipping vulnerabilities your scanners do not understand
Krait is an intelligent Application Security Posture Management platform that brings context across code, cloud, APIs, and runtime so teams can focus on real, exploitable risk instead of endless alerts.
Risk overview
Krait Intelligence Layer
Assets analyzed
0
code, cloud, APIs & hosts
Issues correlated
0
de-duplicated across tools
High-risk
0
multi-tenant / API / cloud
Example correlation
Cross-tenant access in billing API
CriticalLinked code, cloud role, and API path. 3 lower-priority findings merged into 1 high-confidence issue.
Built by teams who have run thousands of pentests and lived the pain of alert fatigue.
Your tools are busy. Your teams are still blind
Pentests, scanners, and cloud checks all generate alerts, but they rarely agree on what matters. Teams end up stitching spreadsheets, PDFs, and dashboards just to answer a simple question: Where are we truly exposed?
Noise without context
You see lists of issues, but not how they connect to real users, tenants, or data flows across systems.
Blind spots in authorization
Most tools are not built to understand who should access what, or how one user might cross into another tenant.
Security outside the dev loop
Findings live outside dev workflows, in separate portals, so they are easy to ignore and hard to resolve.
Krait turns scattered signals into a single, contextual view
Instead of throwing yet another scanner into your stack, Krait sits above your tools. It understands how your code, cloud, APIs, and hosts work together, and surfaces only the risks that matter.
Context over count
Krait links findings to services, permissions, and data flows. You see why an issue is risky, not just that it exists.
API & authorization intelligence
Understand which endpoints exist, who can reach them, and how requests can cross user or tenant boundaries.
Workflow-native security
Krait integrates into your repos, CI/CD pipelines, and ticketing tools, so developers address risk without changing how they work.
False-positive reduction
It flags unreachable code paths, irrelevant dependency alerts, and duplicate findings so teams trust what they see.
One platform across your entire application surface
Krait unifies signals from every layer of modern software and turns them into a single, coherent view of risk.
Coverage map
Different surfaces. One intelligence layer.
Code & supply chain
SAST, dependencies, secrets
Understand vulnerable code, dependency risk, and leaked secrets, then tie them to real services and usage.
Cloud & infrastructure
Cloud posture, IAM, hosts
See how misconfigurations, roles, and exposed hosts affect actual applications and data flows.
APIs & services
Inventory, paths, tenants
Map endpoints, roles, and tenant boundaries, and uncover cross-tenant and business logic issues.
Unified risk view
Correlated, de-duplicated
Krait merges overlapping alerts into fewer, higher-confidence issues you can actually prioritize.
You do not need another siloed tool. Krait uses what you already have, and layers intelligence on top of it.
- Connect code, cloud, and runtime without reshaping your stack.
- See cross-cutting risk that single tools cannot expose on their own.
- Give every role a shared picture of exposure.
From alert fatigue to a focused risk queue
Most platforms claim to reduce false positives. Krait goes further by checking if a finding is exploitable in your environment, and whether it overlaps with other signals.
Before Krait
Alerts everywhere, trust nowhere.
- ✕Long lists of SAST/SCA issues with unclear impact.
- ✕Duplicate alerts across tools and environments.
- ✕Teams ignoring dashboards because they do not know where to start.
With Krait
A small, curated queue of real risk.
- ✓Findings grouped by service, owner, and user impact.
- ✓Unreachable or low-relevance dependency alerts suppressed.
- ✓Tickets created where teams already work, with clear next steps.
How Krait works at a glance
Krait does not replace your tools. It connects to them, builds a contextual model of your systems, and feeds high-confidence issues into your existing workflows.
Step 1
Connect your sources
Repositories, CI/CD, scanners, cloud accounts, and ticketing systems wired in with minimal disruption.
Step 2
Build a contextual model
Krait assembles a high-level view of your APIs, services, permissions, and dependencies.
Step 3
Identify meaningful risk
It correlates and prioritizes issues that matter in the context of your users, tenants, and data flows.
Step 4
Feed your workflows
High-confidence issues land in your ticketing and reporting processes, ready to be acted on.
Built for modern security and engineering teams
Different roles, same problem: too many alerts, not enough clarity. Krait gives each team a tailored view on top of a shared source of truth.
AppSec teams
See how APIs, roles, and tenants interact without reverse-engineering every flow from scratch.
- Focus on RBAC and business logic issues.
- Stop chasing low-impact, duplicate findings.
- Explain risk to devs with real context.
Development teams
Get clear, contextual issues in your existing tools, not vague alerts in yet another portal.
- Findings mapped to code, services, and owners.
- Concrete, actionable remediation guidance.
- No change to your day-to-day workflow.
Security leadership
Answer 'Where are we exposed?' with a consistent view across applications and teams.
- Risk grouped by system, team, and severity.
- Trends over time, not just point-in-time reports.
- Evidence you can share with stakeholders.
Built by security experts
Our team brings together decades of experience in application security, penetration testing, and building tools that solve real problems.
Md Tarek Siddiki
Founder
Elizabeth Ramirez
Co-Founder
Nayeem Uzzaman
R&D Engineer
Annoor Sharara
Product Manager
Tasfia Tabassum
Software Developer
Jinia Tabassum
Compliance Expert
Al Mubin Nabil
R&D Engineer
Got questions? We have answers
Everything you need to know about how Krait works, what data we access, and how we keep your systems secure.
How does Krait access my data, and what level of access is required to perform security scans?
Krait accesses your data using secure, read-only integrations based on the permissions you explicitly grant during setup. Access is limited strictly to what is required to perform security analysis, such as reading repository metadata, dependency manifests, or configuration files. Krait does not modify your code, write back to your repositories, or access data outside the approved scope, and all access is governed by OAuth permissions, audit logs, and strict isolation controls.
How does Krait know which vulnerabilities actually matter?
Krait identifies what vulnerabilities truly matter by building a dependency graph of your application to understand how packages and functions are actually used. Instead of flagging issues based only on the presence of a vulnerable dependency or its CVE score, Krait analyzes whether the vulnerable code paths are genuinely called, reachable, and relevant in your specific context. By considering how and where a function is used within your application, Krait focuses on real risk rather than theoretical exposure—helping teams fix the issues that actually impact security.
Does Krait store my code, data, logs, repository content, or scan outputs?
No. Krait does not store your source code, repository content, or configuration files. All analysis is performed in-memory during the scan. Only minimal, non-sensitive metadata required to present findings, such as issue identifiers, severity, and timestamps, is retained, and this data is handled in accordance with Krait's strict data retention and privacy policies.
How does Krait find Broken Access Control issues?
Krait addresses broken access control by building an API inventory and API access trees to understand how requests and permissions flow through the system. It uses a multi-agent framework to trace user-controlled inputs across the call graph, analyzing role-based access control and related risks such as SSRF, SQL injection, and remote code execution. A validator agent filters out false positives, and Krait uses repository context to suggest targeted code fixes for each issue.
Does Krait use my data or source code to train large language models (LLMs)?
No. Krait does not use your data, source code, scan results, or metadata to train large language models. All analysis is performed solely to deliver security findings for your environment, and customer data is never reused for model training, shared across tenants, or incorporated into any external or internal AI training pipelines.