KDM Analytics: Reducing Financial Risk with Automated Cyber Risk Assessment using AI

Manual cybersecurity assessments are increasingly outdated. These methods are slow, susceptible to human error, and cannot effectively process the vast amount of data necessary for cybersecurity analysis. This leads to incomplete or inconsistent results, leaving organizations and their critical systems vulnerable. Companies need a more robust approach. Automated solutions powered by digital engineering frameworks and AI algorithms offer precise and scalable risk assessments. This proactive, structured, and repeatable approach saves time, reduces costs, and significantly enhances security. It’s the key to achieving efficient, accurate, and reliable cybersecurity management—a necessity in today’s digital risk landscape.

KDM Analytics, a leader in cyber-risk management, stands out for providing a completely automated approach to cybersecurity risk assessment. Its digital engineering framework extends into comprehensive risk assessments, taking a proactive stance against cyber threats.

“We differentiate ourselves through efficient automation, offering a preventive shield against cyberattacks,” says Djenana Campara, president and CEO of KDM Analytics.

KDM Analytics utilizes AI-powered algorithms within a digital engineering framework to deliver structured and precise cyber risk evaluations. The company’s products automatically generate systematic and comprehensive attack trees. For each attack path in the tree, they identify system exposures that would make the attack successful given a particular threat source— pinpointing potential attack paths and exposures before they can be exploited. By proactively mitigating these attacks, KDM Analytics helps businesses safeguard against devastating financial losses.

The company’s Blade RiskManager (BRM) and Blade OneReport (BOR) products go even further. The solution not only identifies at-risk areas and their root causes through comprehensive assessments but also generates detailed attack vector graphs, providing a clear visualization of potential system breaches and recommending courses of action. Security officers can rapidly understand their system’s risk profile and focus resources on the most critical assets and their risk mitigations.

BRM reduces the time and cost of cyber risk assessment by automating analysis and serving as a centralized source for managing and tracing evidence related to operational and system risks. Using a model-based system engineering (MBSE) approach, BRM interprets the target system’s architecture, incorporating detailed information on hardware, software, data flows, and interconnections.

BRM then automatically creates threat and risk models, develops attack trees that show all possible attack paths, and highlights the conditions for success. It identifies specific weaknesses associated with each attack path and assesses the likelihood and potential damage of exploitation, ensuring mitigation efforts are directed toward areas with the highest impact.

Once BRM has prioritized potential vulnerabilities, BOR takes over. BOR is a vulnerability analysis and detection platform that enhances assessment accuracy and unifies outputs from multiple vulnerability analysis tools into one comprehensive report. The report is imported back into BRM for recalculated residual risk.