ThreatQuotient’s Perspective on Security Operations and vision for AI in the ThreatQ Platform We’re all familiar with the “speed of cybersecurity”, the rapid pace at which cyber threats evolve and the corresponding need for timely and agile defense measures. We’ve been trained to understand the importance of quickly detecting, analyzing, and mitigating cyber risks to stay ahead of attackers and protect assets in an increasingly dynamic and interconnected world.

The persistent shortage of skilled cybersecurity professionals which grew by 26.2% over the past year, coupled with the fact that global cyberattacks increased 38% in 2022, means organizations are increasingly looking to security automation to help the cybersecurity teams keep pace. In fact, 98% of respondents to our “2022 State of Cybersecurity Automation Adoption” survey say they have increased their automation budgets.

In today’s digital world, cybersecurity threats are on the rise, and businesses must stay vigilant to protect their sensitive data from cybercriminals. To combat these threats, companies often rely on cybersecurity information vendors to provide them with intelligence information. However, the quality of the intelligence information provided by these vendors has come under scrutiny, and there have been concerns about the accuracy and effectiveness of the information provided.

Every five to ten years, major technology shifts change the way that vulnerability assessment and the related IT risk mitigation processes are approached or implemented. What has remained constant is the formula we use to measure risk and thus prioritize and triage vulnerabilities. Risk = (Likelihood of event) * (Impact of consequences) It’s an approach that intuitively makes sense, but there have been two challenges with how this formula has been applied.