01
AI-generated code
More machine-written code, more inherited patterns and semantic risk. AI coding assistants accelerate delivery but multiply the attack surface faster than traditional AppSec tools can keep up.
Application Security, Proven.
Expose real risk. Fix what matters most. Reduce remediation time and cost.
CyberSagacity validates findings from application security tools, identifies testing coverage gaps, and prioritizes vulnerabilities by likelihood, business impact, and financial exposure—across human-written, open-source, and AI-generated code.
Give development, security, and business teams—and the AI agents and workflows they use—clear direction on what to fix first and why. Focus engineering effort where it reduces the most risk, and measure the results in business terms.
RiskValidated exposure
ROIRemediation economics
CostTool optimization
ProductivityDeveloper focus
ConfidenceDefensible decisions
The problem
More security findings. Less clarity on what to fix.
Application security tools and AI agents can produce conflicting, duplicate, or inaccurate findings—wasting remediation effort and obscuring real risk.
CyberSagacity produces validated, risk-ranked application security intelligence so teams, AI agents, and automated workflows know what to fix first—and why.
Existing tools fall short
They produce false positives and miss meaningful defects. They disagree on severity across tools. They fail to connect findings to business, compliance, and financial exposure.
Aggregation doesn't fix it
ASPM and ASOC platforms aggregate findings and add workflow visibility, but aggregation does not correct inaccurate source data. Bad data just becomes easier to see.
0%
of reported defect types are unimportant in context
0%
have no common attack vector
0%
are reported with low confidence
0%
carry severe consequences — easy to miss in the noise
Source: CyberSagacity AST study — 57 AST tools, 19 languages, 67K+ rules, 497 CWEs, 35M+ curated defects. Read the study →
Why now
More dependencies. More complexity. More AI-generated code. Know where the real risk is.
Open-source dependencies, cloud-native systems, and AI-generated code expand what teams must secure. More findings don’t make priorities clearer.
CyberSagacity produces application security intelligence that validates findings, ranks remediation priorities, and quantifies financial risk exposure—giving teams, AI agents, and automated workflows clear direction on what to fix first, with evidence to explain why.
02
Open-source sprawl
Expanding dependency trees widen the exploitable surface. Every transitive dependency is a potential entry point — and most scanners miss the ones that matter.
03
Cloud-native complexity
Enterprises still own application-layer defects. Cloud providers secure the infrastructure, but the code your teams ship is your responsibility — and it is more distributed than ever.
04
Accountability
Regulators, insurers, and boards require quantified evidence. Severity labels and alert counts no longer satisfy compliance or due-diligence requirements.
Proof & credibility
30 years of application security research. Measurable business outcomes.
CyberSagacity validates security findings, identifies coverage gaps, and prioritizes remediation by business impact across your software portfolio, however the code is created or sourced—helping teams reduce remediation time and cost while lowering application risk. A common set of risk, productivity, and ROI metrics helps security, engineering, and leadership measure and monitor AppSec outcomes.
Our documented use cases report 5×–100× productivity gains and 3×–10× improvements in AppSec ROI.
100:1
Reduction from raw "severe" findings to true severe defects (nearly 2M defects analyzed)
134
Severe defects found mislabeled as minor
58 tools · 19 languages
67,000+ rules
67,000+ rules
Release 2.0, live in customer validations
The products
SATraits™ assesses coverage and risk. SATriage™ delivers shared intelligence for action.
SATraits assesses application security testing coverage and application risk. SATriage validates defect and vulnerability findings, prioritizes remediation, and quantifies financial risk exposure.
Together, they give development, security, and business teams a common language for application risk—and provide teams, AI agents, and automated workflows clear priorities on what to fix first and why.
Assess & plan
Learn more →
SATraits™
Evaluates the effectiveness, coverage, overlap, and accuracy of your AST tools. Exposes false negatives and blind spots.
Prioritize & act
Learn more →
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SATriage™
Converts raw defect data into ranked, business-aligned remediation intelligence. Quantifies financial exposure.
How CyberSagacity works
Ingest findings. Validate accuracy. Add context. Prioritize fixes. Quantify risk.
CyberSagacity products ingest defect and vulnerability findings from security tools, AI, and other sources. They validate and correct findings, add business and compliance context, prioritize remediation by exploitability and potential consequences, and quantify financial risk exposure.
The result is application security intelligence that tells teams, AI agents, and automated workflows what to fix first—and why.
What is realValidate and correct findings before prioritization.
What mattersRank by exploitability and consequence.
What it costsQuantify financial exposure and ROI.
What to do next1-to-N, business-aligned remediation.
01
Ingest
AST and AI-AppSec sources.
02
Correct
Normalize identities, metadata, and classifications.
03
Connect
Correlate defects to assets, controls, and business context.
04
Prioritize
Exploitability + impact + compliance + dollars.
05
Prove
Audit- and executive-ready evidence.
Risk IntelligenceFinancial IntelligenceCode IntelligenceTool IntelligencePolicy Intelligence
CyberSagacity prioritizes every defect using four complementary approaches — statistical risk, financial loss and ROI, mission-critical consequence and attack risk, or any combination — so decisions rest on technical, operational, and financial risk, not a severity label.
Built for every decision-maker
One source of truth. A common language for every role.
For executives
Cyber-risk quantification, financial-loss modeling, and audit-ready evidence.
For AppSec teams
Validated, correlated findings across every tool, with coverage and false-negative intelligence.
For developers
A ranked, deduplicated list of what actually needs fixing, delivered inside your tools and IDE.
For AI agents
Decision context through MCP, open APIs, and policy guardrails.
Validated. Prioritized. Dollarized.
Three things every AppSec decision should be.
Validated.
Every finding is statistically corrected and confirmed before it is prioritized or reported.
Prioritized.
Defects are ranked 1-to-N by exploitability, consequence, and compliance impact.
Dollarized.
Exposure, expected loss, remediation ROI, and residual risk in the language boards and CFOs use.
Why CyberSagacity
Not another scanner. The decisioning layer above all of them.
AST tools find defects. ASPM and ASOC tools aggregate findings. CyberSagacity determines what is true, what matters, what it costs, and what to do next.
| AST / AI discovery | ASPM / ASOC | CyberSagacity | |
|---|---|---|---|
| Signal quality | Raw findings | Aggregated findings | Validated ground truth |
| Prioritization | Severity labels | Routing rules | Exploitability + impact + ROI |
| Proof to exec / audit | Alert counts | Dashboards | Evidence-grade reports |
| Tool overlap / gaps | Unknown | Partially visible | Quantified and validated |
Deploy as SaaS, private cloud, hybrid, or on-premises — including secure environments.
Leadership
Leadership & expertise
Dr. Chris NearFounder & CTO
30 years of AppSec research; patented defect intelligence.
James CannavinoCEO
Enterprise software leader; global scaling and partnerships.
Peter DoughertyPresident & COO
Cybersecurity GTM, exits, partnerships, and capital formation.
Dr. Andrew KaslowSVP, Business Development
Enterprise and government partnership development.
Thomas WolfVP, Software Engineering
Cloud-native platform, AI product, DevSecOps, SaaS delivery.
Research & insights
Independent research, straight from the source.
Research
Just How Noisy Are ASTs?
Chris Near's Black Hat research on AST accuracy and coverage.
Read the study →
Executive brief
Application Security's Alphabet Soup
An executive guide to AST, ASPM, ASOC, and what actually reduces risk.
Download →
Newsletter
The CyberSagacity Signal
Perspectives on turning AppSec findings into decision-grade intelligence.
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