CyCognito Attack Surface Management Platform

My main use case for CyCognito  is attack surface management. It helps me bridge ethical considerations as it serves as a simulation of what an attacker would do in real life, allowing me to continuously discover and test internet-facing assets without requiring manual scope definition. From a pen testing perspective, I want to automate the reconnaissance and information gathering that I would likely do manually, such as subdomain enumeration, live checks, liveness checking, fingerprinting, and scanning; that is what my test cases have for CyCognito .

A specific example for CyCognito involves a mid-sized fashion and electronics brand where we were delving into their infrastructure. We tried to spin up a staging environment during a payment system migration and noticed that nobody commissioned the staging server. It remained live and was running an older version of the checkout software with an unpatched vulnerability that the internal security team did not know existed because it was never added to the asset inventory. Traditional scanners work from inside out, depending on a known asset list, but CyCognito works from outside in, observing the attack surface as an attacker would do and discovering unknown exposed assets, including shadow IT and forgotten infrastructure. We began by inputting the company name to find the staging checkout through certificate transparency logs and DNS enumeration, leading to the identification of an orphaned checkout-related subdomain that surfaced as a critical risk handling payment functionalities with an outdated stack. We achieved all of this before an attacker could exploit it.

Another aspect of my use case for CyCognito is related to compliance with PCI DSS, which is regulated by the Central Bank of Nigeria. For any e-commerce platform accepting credit card and debit card information, we must comply with this guideline. We handle card payment and subject it to PCI DSS requirements, knowing that every system in our cardholder data environment must comply with this policy. With CyCognito's asset inventory, we are able to support that requirement by continuously surfacing what is internet-facing and what touches our payment flows.

CyCognito has impacted us positively in several ways. The first benefit I would mention is that it has helped us with automated risk profiling. We can map our entire ecosystem while detecting unknown and obsolete data across cloud, partner, and subsidiary environments, which has helped us prioritize vulnerabilities by tracing them back to their origin while providing remediation guidance for our security team. Additionally, it integrates with existing tools, aiding us in minimizing manual investigation and administrative workflow.

Since using CyCognito, we have observed specific outcomes. For the last operational year, we had about 30 to 50 percent more risk surface with hidden internet-exposed assets that traditional internal scanning tools missed. MTTR was reduced by more than 60 percent, compressing timelines to patch critical vulnerabilities from weeks or months down to days. The prioritization engine filtered out noise, forcing the security team to focus on a small fraction of exploitable risks. We also saw a less than 5 percent false positive rate, which eliminated hours of manual validation work. Although I prefer to keep financial figures private, we have saved significantly annually due to automated external testing, reducing reliance on manual penetration testing and expensive external bug bounty programs. Other outcomes included footprint reduction by identifying and decommissioning many obsolete or unmanaged devices that posed high exposure, consolidating IT environments into a single risk dashboard without needing local teams to install or host software, and converting technical vulnerability data into a clean security grade framework that allows leadership to manage immediate risk reports effectively.

The best feature CyCognito offers is the zero-input asset discovery, which stands out to me the most. Other EASM tools on the market require you to provide something to start with, such as a list of IP ranges or known domain names, but with CyCognito, we can begin with just our organization name and it will find everything from there, including assets we did not know existed before. The practical significance of this feature enables real users to experience ease in starting with CyCognito, unlike other tools that only surface known items. This matters because there might be forgotten staging environments or subdomains spun up by regional teams that represent entry points leading to possible breaches—things invisible to tools that only scan when prompted. From a pen testing perspective, CyCognito automates the entire reconnaissance phase continuously and at scale, helping us understand potential vulnerabilities beforehand and mitigating against any potential breaches.

A smaller feature I find handy in CyCognito is risk prioritization, along with DAST testing. I rate those as standout features based on my honest review and experience with CyCognito. Another important feature is its automated offensive security testing paired with AI-driven attack path mapping. Unlike basic vulnerability scanners such as Nmap  or Nessus, it simulates adversary reconnaissance to discover shadow IT, analyze interconnected weaknesses, and map out the exact path an attacker could exploit. The AI-powered search and CyQL leverage natural language processing, allowing us to input simple conversational queries that help navigate complex data without needing to know database syntax. Actionable reporting and auto-validation are also standout features. With CyCognito, we can generate reports that highlight vulnerabilities from an attacker's perspective, complete with CVE IDs and remediation advice, and it automatically rescans after fixes are applied, shifting vulnerability status from open to closed without manual intervention.

Regarding CyCognito's AI functionalities, I believe it has shifted from passive asset logging to active continuous risk mitigation. It acts as a security protector for enterprise AI infrastructure, leveraging internal AI agents to automate governance and workflow. For example, while complying with PCI DSS guidelines to safeguard customer payment data online, CyCognito has been immensely beneficial. It also supports shadow AI discovery by identifying exposed and unmanaged machine learning infrastructure. While traditional scanners focus on known software bugs, CyCognito AI uncovers severe operational logic flaws such as misconfigured API access or excessive privileges. Instead of making assumptions based on version numbers, CyCognito uses automated test paths to verify whether systems can be breached externally, mapping to strict inventory mandates. The AI model analyzes metadata, hosting data, and system architecture to intelligently link rogue AI systems to the appropriate departments without human intervention, using a multi-step autonomous agent to demonstrate how attackers could breach defenses. This also helps identify potential AI governance risks such as data exposure, requiring strict gateways to prevent sensitive data loss during testing.

Regarding CyCognito AI capabilities and its accuracy and reliability of output, I think the asset attribution and relationship mapping are particularly strong. The platform employs machine learning to link forgotten digital assets back to the correct corporate subsidiary by analyzing multilingual corporate text and global financial filings, achieving high accuracy. Its accuracy also results from verification through active testing, as CyCognito does not rely solely on software version numbers for its data.

I believe CyCognito could improve by lowering the false positive rate.

When false positives occur with CyCognito, we experience frustrating delays in customer support. Standard support tickets for complaints often take too long to resolve. Additionally, the remediation workflow guidance is sometimes criticized for lacking clear step-by-step instructions on fixing vulnerabilities, requiring manual engineering research. Adding specific code fixes or configuration lines could prevent teams from manually figuring out corrections. Moreover, the tool is somewhat expensive, particularly for small to medium businesses such as ours, pushing smaller IT teams to utilize lower-cost or open-source alternatives. Ideally, a pricing tier favorable to these smaller organizations would be beneficial. I also suggest a consumption-based model based on testing frequency rather than total asset count to enhance platform accessibility. CyCognito also lacks native compliance questionnaire management and vendor scorecard tracking, which could improve overall utility. Finally, while it maps third-party parameters, it cannot fully address lateral movement paths as some other scanners can, requiring reliance on external API integration.

I have been using CyCognito for the past two years.

Evaluating dynamic or multi-tenant cloud environments can blur reliability, and long-term reliability may depend on feedback loops that convert active discoveries into static tests.

When false positives occur with CyCognito, we experience frustrating delays in customer support. Standard support tickets for complaints often take too long to resolve.

CyCognito is deployed in our organization as a multi-tenant public cloud SaaS platform. It analyzes our infrastructure from an outside-in attacker perspective, so it is not deployed as a private cloud or on-premises installation. Its design requires no agents or local software configurations in our data centers. CyCognito's scanning nodes are globally distributed across the internet, remaining outside our network boundaries to accurately represent a real-world attack scenario.

Even though the CyCognito management platform resides in a public cloud, it actively maps and tests our assets regardless of their physical hosting locations.

I give this review a rating of 7.