Assessing risk in application security involves prioritizing which vulnerabilities or threats deserve the most attention, analyzing the nature
of those vulnerabilities, and understanding the context around them. GenAI can streamline and enhance this risk assessment process by
automating many of its components:

• Risk Prioritization: Security teams are often overwhelmed by lengthy lists of vulnerabilities (from scans, pen tests, bug bounty
  reports, etc.). GenAI can act as an intelligent filter, quickly sorting and ranking these issues. By considering factors such as severity
  of the vulnerability, ease of exploitation, and the importance of the affected system, an AI model can assign priority levels to each
  finding. For example, an AI might determine that an SQL injection flaw in a customer login service is a top priority (high impact on a
  critical system), whereas a misconfiguration on an internal test server is lower priority. This automated triage helps focus human
  effort on the most pressing risks first.
• Vulnerability Analysis: Beyond just reading off a CVE description, GenAI can deeply analyze vulnerabilities. It can ingest details
  about a specific flaw and cross-reference it with the application’s code or configuration to determine if the application is truly affected.
  For instance, if there’s a known vulnerability in a library, the AI can check if the application uses that part of the library and in what
  context. It can also summarize what the vulnerability allows an attacker to do. This contextual analysis helps security teams
  understand each finding – not just that it exists, but what it means for their particular application. Moreover, AI can suggest
  remediation steps by drawing on databases of fixes or past knowledge (e.g. recommending a version upgrade or a code patch)
• Contextual Risk Assessment: GenAI can take into account the environment and usage context to refine risk evaluations. This
  means looking at where the vulnerability exists and who could exploit it. If a web application vulnerability is exposed to the internet
  and tied to sensitive data, the AI will rate its risk higher due to the greater exposure and impact. On the other hand, an identical
  vulnerability on a strictly internal tool might be rated lower. AI can also incorporate real-time threat intelligence – for example, if a
  particular exploit is actively being used by attackers in the wild, the AI can raise the risk level for any related findings in the organization’s systems. By automatically weaving together context (asset criticality, data sensitivity, threat actor interest, etc.), GenAI
  provides a more nuanced risk assessment than severity scores alone. 

Examples: To illustrate, imagine a security team receives a scan report with 500 findings. Instead of manually combing through it, they
feed it to a GenAI-powered risk assessment tool. The AI immediately highlights 10 issues as “critical,” complete with plain-language
explanations: “These issues involve the customer database and can be exploited over the internet. They could lead to large-scale data
leakage.” It might also note that among those, two vulnerabilities are in components that have known exploits available, suggesting they
pose immediate danger. For each of these top issues, the AI offers a brief mitigation plan (such as “Apply patch X to the payment
service” or “Implement input validation on the user profile form”). In this way, the security team quickly understands which threats to
tackle first and how, dramatically reducing the time from discovery to mitigation.

Structured Storage and Semantic Knowledge: A critical aspect of automated risk assessment is the systematic storage and organization of threat modeling artifacts. By leveraging Git repositories – either alongside the application code or in dedicated sidecar repositories – teams can maintain a complete historical record of their threat analysis. These artifacts should be structured according to well-defined JSON schemas, making them both machine-readable and human-interpretable. Furthermore, the use of Semantic Knowledge Graphs (or Semantic Threat Modeling graphs) provides a powerful framework for understanding the relationships between various security concerns, attack vectors, and system components. This semantic approach enables teams to trace the lineage of security decisions, understand the impact of changes, and maintain a clear chain of reasoning in their threat assessments.

By automating pieces of risk assessment, AI not only saves time but also helps ensure that critical details don’t slip through the cracks. It
provides a second pair of eyes — tireless and informed by vast data — to catch what humans might miss or to connect dots that aren’t
obvious at first glance. The result is a more proactive and informed AppSec program where potential security threats are understood and
addressed in the appropriate context before they can be exploited.