Invicti
Pros
- Deep, out-of-the-box integrations with CI/CD pipelines and issue trackers enable a fully automated, closed-loop “scan-to-ticket” workflow.
- It supports scanning non-public, internal applications through the use of an on-premises or VPC-deployed scan agent.
Cons
- The vulnerability portfolio is limited to publicly known tests, which means it is not designed to find novel, 0-day, or non-CVE flaws.
- The platform requires a significant upfront time investment in configuration before its full value is realized.
Detectify
Pros
- Its unique vulnerability intelligence is sourced from a private, vetted network of elite ethical hackers, enabling it to find novel, non-CVE flaws.
- A proprietary dynamic API fuzzing engine tests APIs with a massive, randomized payload library to find vulnerabilities that static, schema-based checks miss.
- The modern SaaS-based platform is designed for rapid time-to-value, allowing an engineer to get started and find assets in minutes.
Cons
- The platform is SaaS-only and does not offer an on-premise solution or local agent, making it unable to scan non-public, internal networks.
- Creating custom, application-specific tests is not a self-service feature and requires engaging Detectify’s internal team as a managed service.
In-depth comparison: Visibility and Context
Both Invicti and Detectify are engineered to create visibility by tackling shadow IT, using External Attack Surface Management (EASM) to continuously monitor public sources like DNS records and transparency logs to discover unknown web assets. The fundamental difference lies in what they do next. Invicti employs a predictive, data-driven model, using AI to score an asset’s potential risk before a deep scan is run. Detectify uses a hacker-driven, contextual model, prioritizing assets based on immediate, actual findings it discovered in its vulnerability testing.
Invicti’s approach of predictive risk scoring is designed to solve for scan prioritization at scale. When a new asset is discovered, the platform runs a lightweight analysis. This data is fed into a proprietary AI/ML model that uses over 200 parameters (like outdated server software or insecure cookie configurations) to calculate a score that predicts the likelihood of the asset having critical vulnerabilities. This automatically creates a prioritized work queue (e.g., Critical, High, Low), recommending where AppSec teams should focus their limited scanning resources first.
In contrast, Detectify employs a guided action approach. Its Surface Monitoring product first discovers and classifies web assets, then provides intelligent recommendations on which assets should be targeted for deeper DAST scans using its Application Scanning product. This “discover-and-recommend” model allows security teams to prioritize deep-scanning resources on the most critical or high-risk assets, avoiding the slower and costlier process of automatically scanning every discovered asset.
Detectify is built for the team that wants to prioritize from an attacker’s perspective, focusing on emerging threats and business function. It provides immediate, contextual alerts on what hackers would find first, allowing teams to prioritize based on a combination of real-world findings and business impact.
In-depth comparison: Assessment
When comparing assessment methodologies, both Invicti and Detectify represent different approaches to DAST (Dynamic Application Security Testing). The fundamental difference lies in what they test for and how they execute their testing. Invicti’s value is its automated confirmation of known vulnerabilities, while Detectify’s value is its discovery of known and novel vulnerabilities sourced from elite hackers, its internal security research team, and Alfred, its AI Security Researcher.
Invicti’s entire testing engine is built around its attempt to prove that a vulnerability is present in its user’s system. This methodology is an attempt to solve the false positive problem. When the scanner infers a potential vulnerability (e.g., via an error message or a time-based delay), it automatically launches a second, non-destructive exploit to prove the vulnerability is real. However, its test portfolio relies only on publicly available tests, meaning it’s only as effective as what is known.
Detectify uses payload-based testing to confirm exploitability, but its core differentiator is its vulnerability sourcing and its own proprietary engines. Its test intelligence comes from a multi-source model: an internal research team, an AI agent named “Alfred” that auto-generates tests from new CVEs, and the Detectify Crowdsource network of private, vetted ethical hackers. This model allows Detectify to find novel, 0-day, and non-CVE flaws that public test suites miss, with the platform claiming a significant percentage of its tests are for vulnerabilities not covered by CVEs. For API testing, Detectify uses a proprietary “dynamic fuzzing” engine that probes APIs with randomized and rotated payloads from a massive library (e.g., 330,000+ for command injection) to find flaws that static, schema-based checks would miss.
Detectify is built for the team that wants to find what other scanners miss; its value is in its unique, hacker-sourced intelligence, providing a higher-fidelity signal on the novel, emerging, and high-impact vulnerabilities that real-world attackers are actively exploiting.
In-depth comparison: Usability
Onboarding with Invicti is an exercise in system integration, reflecting its “shift-left” philosophy. The setup is geared towards connecting the scanner to the existing SDLC. This involves configuring CI/CD pipeline triggers (like Jenkins or GitLab), integrating with issue trackers (like Jira or Azure DevOps), and, crucially, setting up complex authenticated scans. The platform’s ability to handle SSO, MFA (via TOTP), and custom-scripted login sequences is a core feature, but it requires an upfront investment of time and technical expertise. The usability “win” is not a 10-minute setup, but the investment in getting the tool up and running.
Detectify is engineered for a “workflow-driven” SaaS experience that prioritizes speed and clarity. The onboarding process is designed to take minutes, not days. The primary flow involves connecting a cloud provider or entering a root domain, then immediately activating “Surface Monitoring” to discover the external attack surface. The platform provides immediate value by discovering assets and then providing intelligent scan recommendations. This “clean, intuitive interface” guides the user logically from discovery to actionable, hacker-sourced findings, a process supported by CSMs and CSEs to ensure rapid adoption.
Conclusion: Which product should I choose?
The choice between Detectify and Invicti represents a clear decision between a forward-looking, hacker-centric platform and a traditional tool. Detectify clearly stands out with its modern, SaaS-based approach, designed for rapid time-to-value and a true attacker’s perspective. Its core value—which legacy scanners cannot match—lies in its proprietary, multi-source intelligence model. By leveraging its Detectify Crowdsource network, an AI researcher, and an internal team, it uniquely finds the novel, non-CVE vulnerabilities and emerging threats that real-world attackers exploit. This, combined with its powerful API fuzzing engine, makes it the definitive choice for securing the modern, external attack surface. Invicti, in contrast, remains a tool focused on internal automation, limited by a test portfolio of only publicly known flaws and a significant configuration burden, leaving its users blind to the unknown and emerging threats that Detectify is built to find.
