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Bright vs Invicti - Bright SecurityBright Security Bright vs Invicti - Bright Security

A TECHNICAL COMPARISON FOR MODERN APPLICATION SECURITY TEAMS

Dynamic application security testing is only valuable when findings reflect real, exploitable risk. As applications shift toward APIs, microservices, and CI/CD-driven delivery, traditional crawl-based scanners struggle to provide a reliable signal.

This page provides a technical comparison between Bright (STAR) and Invicti, focusing on validation depth, coverage of modern attack paths, and operational impact on engineering teams.

Bright-vs-Invicti Comparison
Barracuda
SentinelOne
MetLife
Nielsen
ABInBev
Heritage Bank
Barracuda
SentinelOne
MetLife
Nielsen
ABInBev
Heritage Bank

How the Two Approaches Differ at a Technical Level

Invicti is a traditional DAST platform that relies on crawl-based scanning and heuristic validation techniques. While it attempts to reduce false positives through confirmation logic, testing remains largely constrained to reachable, crawlable surfaces.

Bright STAR performs runtime, exploit-based testing, validating vulnerabilities only when they are confirmed through real execution paths. This enables deeper visibility into APIs, logic flaws, and non-crawlable attack surfaces.

This difference in testing model has a direct impact on signal quality, remediation confidence, and CI/CD velocity.

Core Technical Comparison

Scan Execution Model

Runtime DAST with exploit validation
Crawl-based DAST scanning
Executes real attack paths against running applications and APIs
Limited execution beyond discovered surfaces
Built for continuous execution inside CI/CD
Typically run as scheduled or gated scans

Accuracy & Signal Quality

Confirms exploitability before reporting
Heuristic-based confirmation
Less than 3% false positives
Reduced false positives compared to legacy DAST
Findings tied to verified attack paths
Limited validation for complex workflows and APIs

Coverage of Modern Application Risks

API security (BOLA/BOPLA)
Traditional web vulnerabilities
Business logic flaws
Limited API and logic-flow coverage
Multi-step attack chains
Relies heavily on crawler reachability
Shadow and undocumented endpoints

Remediation & Validation

AI-assisted remediation guidance
Manual remediation workflows
Automatic re-validation after fixes
Re-scanning required to validate fixes
Confirms vulnerabilities are actually resolved
No automated validation loop

Developer Workflow Impact

Pull-request level automation
Findings require manual triage
Low-noise findings delivered to dev tools
Security teams filter results before dev action
Only actionable, verified issues
Prioritization based on severity scoring

CI/CD Integration

Native CI/CD execution with security gates
CI/CD support via scan triggers
Enforcement based on verified exploitability
Decisions based on severity scoring
MCP-based policy enforcement
No exploit-validated gating
Core Technical Comparison

Scan Execution Model

Runtime DAST with exploit validation
Executes real attack paths against running applications
Integrated directly into CI/CD pipelines
Static and dependency-based analysis
No runtime execution or exploit confirmation
Typically, post-build or asynchronous scans

Accuracy & Signal Quality

Validates findings through real exploitation
<3% false positives due to proof-based detection
Integrated directly into CI/CD pipelines
Pattern and rule-based detection
Higher false positives requiring manual review
No confirmation of exploitability

Coverage of Modern Application Risks

Business logic flaws
BOLA / BOPLA
Multi-step attack chains
Shadow and undocumented APIs
GenAI-generated code paths
Known vulnerability patterns
Dependency and code-level issues
Limited visibility into runtime logic and API abuse

Remediation & Validation

AI-assisted remediation
Automatic re-validation after fixes
Confirms vulnerability is fully resolved
Manual remediation workflows
No runtime re-validation
Relies on code changes alone for closure

Developer Workflow Impact

Pull-request–level automation
Low-noise findings delivered to dev tools
Only actionable, verified issues
Findings require manual triage
Security teams filter results before dev action
Prioritization based on severity scoring

CI/CD Integration

Native CI/CD execution with security gates
Enforcement based on verified exploitability
MCP-based policy enforcement
CI/CD support via scan triggers
Decisions based on severity scoring
No exploit-validated gating

Operational Outcomes

Capability
Bright
Snyk
Testing Method
Runtime exploit-based DAST
Crawl-based DAST
Exploit Validation
Verified at runtime
Heuristic confirmation
API Coverage
Strong
Limited
Logic Flaw Detection
Yes
Limited
False Positives
<3%
Lower than legacy DAST
CI/CD Impact
Minimal
Moderate
Fix Verification
Automatic
Manual
Bright
Snyk
Testing Method
Runtime exploit-based DAST
Crawl-based DAST
Exploit Validation
Verified at runtime
Heuristic confirmation
Logic Flaw Detection
Yes
Limited
False Positives
<3%
Lower than legacy DAST
CI/CD Impact
Minimal
Moderate
Fix Verification
Automatic
Manual

When Teams Choose Bright Over Invicti

Organizations typically adopt Bright when they require:

Verified, exploitable findings only

Strong API and business logic coverage

Faster feedback inside CI/CD

Reduced dependency on crawlability

Higher confidence in remediation outcomes

Summary

Invicti improves upon legacy DAST by reducing false positives through heuristic confirmation. Bright STAR goes further by validating vulnerabilities through real runtime exploitation, providing higher confidence, broader coverage, and a cleaner signal for modern application architectures.

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Understand the technical differences behind modern AppSec approaches. See how runtime validation changes accuracy, coverage, and remediation. Go deeper into STAR, MCP, and real CI/CD security enforcement.

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