What frameworks help enterprises quantify and prioritize AI risk?

Frameworks like the NIST AI Risk Management Framework and the OWASP Top 10 for LLM Applications give enterprises structured categories for thinking about AI risk, providing a useful shared vocabulary and a checklist of areas to consider when assessing a given deployment. These frameworks are valuable for establishing a baseline understanding of what categories of risk exist and roughly how they tend to manifest across different types of AI systems.

Where these frameworks fall short on their own is in quantifying actual exposure for a specific organization's specific deployed systems, since a generic checklist cannot tell you whether your particular agent, with its particular tool access and particular data connections, is actually vulnerable to a given category of attack. Two organizations could reference the exact same framework and end up with dramatically different actual risk exposure based on how their systems are configured and what they are connected to.

Continuous red teaming addresses this gap by producing concrete, prioritized findings based on testing against an organization's actual deployed agents, rather than relying on theoretical risk scores derived from generic framework categories. This kind of testing tells you which vulnerabilities are genuinely exploitable in your specific environment right now, which is a different and more actionable piece of information than knowing that prompt injection is generally a risk category worth considering.

Akto's Argus applies this kind of ongoing, environment specific testing to give security teams a prioritized, evidence based risk picture grounded in their actual systems, complementing the structural understanding that frameworks like NIST and OWASP provide with concrete findings specific to what is really running.