Category: Machine Learning

Cyber Security

In InfoSec stress is a given, especially given that the InfoSec team needs to be right every time while bad actors need to be right only once. Vulnerability scanners overwhelm InfoSec teams, since these tools spew out a torrent of vulnerabilities. The whole scanning paradigm has outlived its value, but more about in a separate […]

vulnerabability exploitability

Every piece of code is a potential source of vulnerabilities. This could be operating systems, containers, databases, web servers and the list just goes on. It also includes hardware devices like L2 / L3 network devices, healthcare devices, IOT devices and more. To further compound things, the rate at which vulnerabilities are discovered is growing […]

Data Science and commercially available AI/ML implementations now make it possible to predict whether a vulnerability can be weaponized into malware. This could be a critical moment in cybersecurity as it allows vulnerability management to be truly proactive and reduces the remediation workload. But why bother with this? And even if we did, how could […]

Priority

The number of vulnerabilities being reported has just been growing over the years. The below chart help depict how the count of vulnerabilities has grown significantly (though not yet exponentially) over the recent years. Note it is apparent from the chart how ThreatWatch provides better overall vulnerability intel coverage, apart from standard sources like NVD. […]

Most organizations face challenges with prioritizing risk from a new vulnerability or threat. At times, late breaking threats do not provide a severity assessment. The standard way to identify the key characteristics of a threat is using CVSS (Common Vulnerability Scoring System). CVSS provides a Vector (based on key dimensions / attributes of the threat […]