AI-native Application Security Posture Management

Application security that runs itself

A package gets compromised somewhere in the world — Kasuta tells you in minutes whether you're affected, not when you read the blog post. It threat-models like it built your system, lives in your Slack, reviews every pull request, and opens the fix PR for you.

Minutesfrom advisory to "are we affected?"
200+threat intel sources, matched to your SBOMs
Slack-nativeagent that knows your teams & code
kasuta · live pipeline
🧠
29 false positives auto-closed
with reachability evidence attached
🔧
Fix PR #214 opened
SQL injection · self-reviewed · tests pass
Minutes
from a package compromise anywhere to “are we affected?” — answered automatically
0
threat-intel sources monitored and matched against your live SBOMs
0%
of raw findings auto-resolved as noise — with written evidence
100%
of pull requests security-reviewed, with conversation memory
The problem

Your scanners create work.
They were supposed to remove it.

Most security programs are a pile of disconnected tools producing thousands of alerts, most of them false positives, all of them assigned to nobody. Engineers tune them out. Security teams drown in triage. Real risk hides in the noise.

01

Alert fatigue is the default

Raw scanner output is mostly noise. Without reachability and exploitability context, every finding looks urgent — so none of them are.

02

Tool sprawl, zero posture

SAST here, SCA there, secrets somewhere else. Five dashboards and still no single answer to “what’s our riskiest service right now?”

03

Finding ≠ fixing

Tickets pile up for months. The gap between detection and remediation is where breaches live — and traditional tools stop at detection.

The platform

One platform. Every layer of your AppSec program.

Detection is the floor, not the ceiling. Kasuta pairs deep scanning with live threat intelligence, context-aware threat modeling, compliance, and AI agents that act — all wired into how your organization actually works.

Supply-chain defense, in real time

New packages get compromised every week. Threat intel from 200+ sources is matched directly against your bill of materials — so you know whether you're impacted in minutes, not when you read the blog post.

Threat modeling that knows your stack

Most threat modeling tools drown you in false positives because they don't understand your infrastructure. Kasuta connects to your codebase and wiki, models against your real architecture, and publishes back with one click.

💬

A security agent in your Slack

Mention the agent in a thread to trigger a threat model. Ask it your operational questions, get guided remediation, or let it fire the automatic fix — without leaving the conversation.

Deep scanning across everything

Multi-engine code analysis with an independent AI deep-review, dependencies and SBOM, IaC and containers, secrets across full git history, and continuous attack-surface discovery — one pipeline, one verdict per finding.

License compliance, on autopilot

Every component actively tracked and enriched in a continuously refreshed bill of materials. Copyleft and policy-violating licenses flagged before they ship — audit-ready at any moment.

Executive view, org-aware context

Kasuta integrates with your existing infrastructure and team hierarchy. Ask for your top issues and it already knows your org — and connects you directly with the owner of the affected code. Leadership gets dashboards; engineers get exactly what's theirs.

End-to-end coverage

Security at every stage of the lifecycle

Not a point tool. Kasuta is present from the design doc to production — and back again when new threats emerge.

Threat modeling that understands your infrastructure

Generic threat modeling tools bury you in false positives because they don't know your system. Kasuta connects directly to your codebase and wiki, models against your real architecture and real findings, and publishes the result back to your wiki in one click. You can even trigger it from a Slack thread.

  • Grounded in your actual code, infrastructure, and exposure — not checklists
  • One-click publish to your wiki; trigger from Slack with a mention
  • Hours instead of weeks — for every feature, not just the big ones
⚠ Spoofing: unauthenticated callback
⚠ Tampering: unsigned payload path
✔ Mitigation proposed
The difference

AI agents that do the work — not just the alerting

Detection is table stakes. Kasuta’s agents triage, review, verify, and remediate — autonomously, with their reasoning written down for humans to audit.

Triage Agent

False positives die here

Every finding is interrogated before a human ever sees it: Is the vulnerable code reachable? Is the dependency actually in production scope? Is this version really affected? Is there a known exploit in the wild? Kasuta combines deterministic evidence with deep AI reasoning to issue a verdict — and writes down why.

  • Reachability, exploit probability, and active-exploitation signals on every verdict
  • High-confidence noise auto-closed; the rest enriched with remediation guidance
  • Full audit trail — every decision is explainable, every verdict reviewable
HIGH Finding · deserialization of untrusted data
ReachabilityNot reachable from any entry point
ScopeDev-only dependency
Exploit signalNo known exploitation
VERDICT: FALSE POSITIVE confidence 0.97 · auto-closed · evidence attached
PR Review Agent

A senior security reviewer on every pull request

Kasuta reads every diff with full repository context, comments inline like a teammate, and — unlike one-shot bots — remembers the conversation. Push a new commit and it picks up where it left off. Claim something is fixed and it verifies the fix actually closes the vulnerability before agreeing.

  • Risk-aware: deep analysis where it matters, instant pass where it doesn’t
  • Stateful reviews that persist across commits and re-scans
  • Independent fix verification — “fixed” means proven fixed
K
kasuta reviewed · just now

This endpoint interpolates order_id into the query on line 84. With the new route exposing it as a request param, this is injectable. Suggest a parameterized query — example below.

View suggestion·1 of 2 findings
kasuta · after commit 8f3c21d

Verified: the parameterized query resolves the injection path. Marking resolved.

Remediation Agent

The fix PR, opened for you

Kasuta doesn’t stop at “here’s your problem.” Its remediation agent analyzes the affected code, plans the smallest correct change, writes it, validates the syntax, reviews its own diff — and opens a pull request your team just has to approve.

  • Minimal, surgical patches — no drive-by refactors
  • Self-review gate before any PR is opened
  • Related findings grouped into one clean, reviewable change
PULL REQUEST fix: parameterize order lookup queries
- cursor.execute(f"SELECT * FROM orders WHERE id = {order_id}")
+ cursor.execute("SELECT * FROM orders WHERE id = %s", (order_id,))
✓ syntax validated✓ self-reviewed✓ ready for approval
Intelligence Agent

From compromise to “are we affected?” — in minutes

Kasuta monitors 200+ intelligence sources around the clock and maps every new threat directly to your bill of materials. When the next package compromise hits, you know your exposure in minutes — not hours, not days, not when you read the blog post. Curated intel that matters to you, where you’re actually impacted, plus the threat landscape relevant to your industry.

  • 200+ sources — researcher, vendor, and government feeds — not 20
  • Instant “are we affected?” via SBOM matching across every repository
  • Curated to real impact: reachability validated, owners alerted, noise dropped
09:41
Advisory ingestedmalicious release of a popular npm package
09:43
Inventory matched3 repositories include affected range
09:46
Reachability validated1 exploitable path · owners alerted
Slack Agent

Your security team’s newest hire lives in Slack

Mention @kasuta in any thread and it gets to work: trigger a full threat model on the design being discussed, ask any operational question, get step-by-step guidance on fixing an issue — or just tell it to fix the thing. It’s integrated with your team hierarchy and code ownership, so by the time you ask “what are my top issues?”, it already knows who you are and connects you with the owner of the affected code.

  • Trigger threat models from a Slack thread — one mention, no context switch
  • Operational Q&A with full org context: your teams, your services, your code
  • Guided remediation, or automatic fixes triggered on your say-so
JD
jordan · #payments-eng

@kasuta threat model this new payout webhook design ↑

K
kasuta APP · just now

On it. Reading the thread + your payment-service code… Threat model published to your wiki: 3 high-risk paths, mitigations proposed. Unsigned callback risk matches an open finding owned by @priya — looping her in. Want me to open the fix PR?

Yes, open fix PRView threat model
📊

Executive dashboards

Posture rolled up from repository to organization, mapped to your real team hierarchy. Leadership sees risk trends and exposure at a glance; every number drills down to the owning team and the exact finding.

Built for the agentic era

Kasuta exposes its full posture graph through an open agent interface (MCP). Your AI coding assistants and internal agents can query findings, inventories, and risk scores — and trigger scans — as easily as your engineers can.

🛡

Guardrails at the laptop

Lightweight pre-push protection stops secrets and known-vulnerable dependencies before they ever reach your repos — with centrally managed exceptions, offline tolerance, and zero developer-experience tax.

Why Kasuta

Beyond scanners. Beyond dashboards.

First-generation tools detect. First-generation ASPM aggregates. Kasuta acts.

Capability Snyk Semgrep Kasuta
Unified SAST, SCA, IaC, secrets & attack surface
AI triage with reachability & exploitability evidence
Stateful AI review on every pull request
Autonomous fix PRs with self-review
Threat modeling grounded in your codebase & wiki
Threat intel from 200+ sources mapped to SBOMs in minutes
Developer guardrails before code leaves the laptop
Slack-native agent: threat models, Q&A, fixes from a thread
Org-aware context: team hierarchy & code ownership built in
Container image scanning (OS packages, base images)
Custom rule engine (write your own detection logic)
Ecosystem breadth (1000+ integrations, marketplace)
License compliance tracking (copyleft, permissive, etc.)
Conversational access + open agent interface (MCP)
Native    Partial / add-on    Not available

Snyk and Semgrep are trademarks of their respective owners. Comparison based on publicly available documentation as of June 2026.

FAQ

Questions, answered

How fast will we know if a compromised package affects us?

Minutes — not hours, days, or when you read a blog post. Kasuta continuously ingests threat intelligence from 200+ sources and matches every advisory against your live software bill of materials. You get a precise answer: which repositories, which versions, whether the vulnerable path is reachable, and who owns the fix.

Why are Kasuta’s threat models more accurate than other tools?

Most threat modeling tools flood you with false positives because they don’t understand your infrastructure. Kasuta connects directly to your codebase and wiki, so its threat models are grounded in your real architecture, real findings, and real exposure — and publish back to your wiki in one click. You can even trigger one from Slack by mentioning the agent in a thread.

Can we run security from Slack?

Yes. Mention the Kasuta agent in any Slack thread to trigger a threat model, ask operational questions like “what are my team’s top issues”, get step-by-step remediation guidance, or trigger an automatic fix. The agent is integrated with your team hierarchy and code ownership, so it already knows you — and routes you straight to the owner of affected code.

Does Kasuta handle license compliance?

Yes. Kasuta actively tracks and enriches every component in your software bill of materials, flags copyleft and policy-violating licenses before they ship, and keeps an always-current inventory you can audit at any time.

How is Kasuta different from running a few scanners in CI?

Scanners produce findings; Kasuta produces outcomes. Every finding is deduplicated, triaged with evidence, mapped to an owner, ranked by real risk, and — where possible — fixed by an agent that opens the PR. The scanners are the start of the pipeline, not the product.

Will AI triage close things it shouldn’t?

Verdicts combine deterministic signals (dependency scope, version validation, reachability, exploit intelligence) with AI reasoning, and every decision ships with written evidence. Only high-confidence noise is auto-closed; everything else is enriched and routed to a human. You can audit any verdict, and confidence thresholds are configurable.

What languages and ecosystems are covered?

Static and AI code analysis cover the major modern stacks — Python, JavaScript/TypeScript, Go, Java, Ruby, PHP, and C# among them — alongside dependency analysis for 20+ lockfile formats, container and Kubernetes configuration, and Terraform.

Can our own AI tooling use Kasuta?

Yes. Kasuta exposes its posture data and actions through an open agent interface (MCP), so IDE assistants and internal agents can query findings, SBOMs, and risk scores or trigger scans programmatically.

Get started

See your real security posture

Get a live walkthrough on your stack — and see how many of today’s “critical” alerts Kasuta would have already closed, fixed, or never raised at all.

  • 30-minute demo, tailored to your environment
  • Pilot on your own repositories
  • No rip-and-replace — runs alongside what you have today

We’ll get back to you within one business day. No spam, ever.