InsightFortify · AI-native application security posture

Thousands of scanner alerts. The few that can actually hurt you.

InsightFortify runs the scanners you trust across code, dependencies, secrets, infrastructure, web apps, APIs, AI models and cloud accounts. It merges what they report into one queue and scores each finding by whether it can be exploited here: known exploitation, internet exposure, sensitive data and runtime evidence. Developers get the fix, not the CVE number.

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66% fewer findings to act on
Runs in your cloud
Every score explains itself

The InsightFortify dashboard: raw scanner findings reduced to unique and then actionable findings, open findings by priority, SLA breaches and the riskiest repositories

Built on the open-source scanners security teams already trust

TrivyOpengrepGitleaksSyft CheckovOWASP ZAPProwlerFalco CISA KEVFIRST EPSSOSV

The problem

Security teams don’t lack findings. They lack a reason to fix one before another.

Every scanner is right about what it sees and wrong about what matters. The same vulnerable library shows up in five tools, a critical CVE sits in a test fixture, and the exposed admin key in last year’s commit is at the bottom of a list of 4,000. Developers stop reading. The board asks whether you are getting safer, and nobody can answer.

Noise

The same issue, five times

SCA, container and SBOM tools each report the same package. Duplicate tickets erode developers’ trust faster than any false positive.

No context

“Critical” says nothing about your environment

CVSS rates the bug, not the deployment. A critical in a dev-only dependency matters less than a medium on an internet-facing service that holds card data.

New surface

AI code and agents arrived faster than policy

Assistants write a growing share of code, teams ship LLM features and agents with tool access, and none of it fits the old scanner categories.

Measured, not promised

From raw output to the list worth fixing

These numbers come from our own demo estate: two sample services, OWASP NodeGoat, a local OWASP Juice Shop under a ZAP scan, and one AWS account. Correlation removes duplicates; context removes what cannot be reached. What remains is what we would ask a developer to fix this sprint.

On NodeGoat alone, 340 raw results became 215 unique findings and then 80 actionable ones.

Raw scanner results

382

Unique, after correlation

287

Actionable, after context

131

66% fewer items to act on. Nothing is deleted: every suppressed or deprioritised finding stays in the queue with the reason recorded.

How it works

Scan → Correlate → Prioritise → Fix

One pipeline for every kind of finding. You don’t replace your scanners or your workflow; you add the layer that decides what to do first.

Scan

Repositories on push or schedule, CI pipelines through one API call, web apps and APIs a security lead has verified as yours, cloud accounts through a read-only role. Or upload results from scanners you already run.

Correlate

Every result is normalised and fingerprinted, so the same issue from five tools, across branches and rescans, is one finding with five sources attached.

Prioritise

A 0–10 score built from named factors: CVSS, CISA KEV, EPSS, internet exposure, sensitive data, runtime evidence, test code and dev-only scope. P1–P4 with an SLA for each.

Fix

The fixed version, the code change or the policy to apply, an AI explanation in plain language, and a CI gate that blocks only what you decide should block.

A score that shows its work

Why this finding is P1, line by line

No black box. Each priority comes with the factors that produced it, so a developer can disagree with a specific line instead of the whole tool.

CVSS 7.5the published base score7.5
Exploited in the wildlisted in the CISA KEV catalog+2.0
Internet-facingthe service is reachable from the internet+1.0
Handles sensitive datapayment card data, PII+0.5
Compensating controlsWAF in front of the service−0.5
Score → P1, fix within 7 days9.6

In the pipeline

One call from any CI system

The pipeline posts its scan results and gets back a pass or fail decision based on your gate policy: new P1s only, no new secrets, nothing on KEV.

# any CI system: scan, then ask for a decision
trivy fs --format json --list-all-pkgs -o trivy.json .
gitleaks detect --report-format json --report-path leaks.json --exit-code 0
jq -n --slurpfile t trivy.json --slurpfile g leaks.json \
  '{repository: env.GITHUB_REPOSITORY, commit: env.GITHUB_SHA,
    outputs: {trivy: $t[0], gitleaks: $g[0]}}' |
curl -sf -H "Authorization: Bearer $FORTIFY_API_KEY" \
  -H 'content-type: application/json' -d @- $FORTIFY_URL/api/ci/scan |
jq -e '.data.gate.result == "pass"'
# fail → "blocking": [{"title": "…", "priority": "P1", "file": "…"}]

Coverage

Code to cloud, in one queue

Each surface uses a proven open-source engine, so you can check our findings against the tool itself. InsightFortify adds correlation, context, prioritisation and the fix.

Code

Source & secrets

Static analysis with Opengrep; secrets with Gitleaks across the full git history, not just the latest commit.

Rotation tracked as a triage action
Test code and fixtures recognised
Your own secret patterns, in CI too

Supply chain

Dependencies & SBOM

Trivy for open-source packages and container images; Syft for CycloneDX SBOMs, with each component searchable across the estate.

“Where do we run log4j?” in one search
Dev-only and transitive scope
KEV and EPSS on every CVE

Infrastructure

IaC

Checkov on Terraform, CloudFormation, Kubernetes and Dockerfiles, so a public bucket is caught at the pull request, before it is deployed.

Benchmark mappings kept for compliance
Same queue, same SLA

Applications

Web apps & APIs

OWASP ZAP dynamic testing against targets a security lead has verified, plus an API inventory built from code, specifications and observed traffic.

Verified, audited targets only
Shadow (undocumented) API detection
Runtime evidence raises the score

AI apps & agents

Inventory of models, prompts and agents in code; prompt-injection and data-leak findings; runtime guardrails and tool-permission policies for agents.

OWASP LLM Top 10 mapping
Agent tool-call audit trail

Cloud

Cloud, identities & runtime

Prowler posture checks, IAM entitlements with blast radius, attack paths from the internet to sensitive data, and Falco runtime detections.

CIS, NIST, PCI, SOC 2 failure view
Least-privilege policy suggestions

Attack paths

Findings that look manageable alone can add up to one critical path

On their own, none of these would top a list. Joined together they are a straight line from the internet to payment data, and InsightFortify ranks the path, not the parts. This is the path in our demo account.

ExposureInterneta public load balancer in front of the service

Workloadpayments-apiits image carries a critical, fixable CVE

Identitypayments-api-taskthe role the task runs as can read the exports bucket

Dataacme-payments-exportsmarked sensitive

The attack paths page: a path from the internet through a vulnerable workload and an over-privileged role to a sensitive bucket — **Attack paths.** Each hop is a finding you can open; fixing any one breaks the chain.

An identity's detail page: blast radius across inventoried resources, privilege escalation routes and a least-privilege policy suggestion — **Identity blast radius.** What a role can reach, how it could escalate, and a least-privilege starting point.

The console

One queue for AppSec, one answer for the board

Analysts triage with the evidence and the score breakdown. Developers see only their services and the fix. Leadership sees the trend, the SLA position and the compliance gaps.

The findings queue: priority, score, kind, title, repository, scanner sources and SLA due date — **The queue.** Every kind of finding, deduplicated, in priority order, with the SLA due date.

A finding's detail page: the score breakdown, evidence, affected location, fix guidance and history — **A finding.** Score factors, evidence, the fix, and every decision taken on it.

For security leaders

Numbers you can take to the board

The questions a CISO is asked are simple. The data to answer them is usually scattered across eight tools. InsightFortify keeps it in one place.

Are we getting safer?

Open P1/P2 trend, new versus fixed, and mean time to remediate by priority and by team.

Are we meeting SLA?

Per-priority SLAs you set, breaches surfaced before an auditor finds them, and every dismissal or snooze recorded with who and why.

Where do we fail audit?

Requirements failed by open findings, by framework: CIS, NIST 800-53, PCI DSS, SOC 2, ISO 27001.

What is our AI exposure?

Every model, prompt and agent in the estate, what each can touch, and what guardrails stopped.

Read the CISO series: landscape, operating model, use cases, first 90 days

Deployment & trust

Your code never leaves your cloud

InsightFortify is a modular monolith on PostgreSQL and Redis. Scanners run as workers next to your code. It is a single product to deploy in your account, not another SaaS that needs read access to every repository.

Isolation

Tenant isolation in the database

PostgreSQL row-level security on every tenant table, so a missed filter in application code still returns nothing from another organisation.

Secrets

Credentials by reference

Cloud roles and tokens are stored as references to your secret manager, never as values. Cloud access is read-only and assumed per scan.

Safety

Scans only what you own

A dynamic scan is an attack. It runs only after a security lead verifies the target is yours to test, and their written attestation is kept in the audit trail.