CISOs are facing a challenge with AI cyber data points created by software solutions used in their organisation to monitor enterprise security. So, how can they explain the AI cyber data to the executive stakeholders and help improve clarity in their decision making?
Plenty of well-established risk domains, such as credit or market risk, are clear to the board because they are expressed in economic terms—revenue gain/loss, value, and operational costs.
With cyber risk, the main issue lies in the risk calculation methods—presenting the actual organisational impact to the board is hard without financial numbers to back up claims.
Cybersecurity specialists have started using AI solutions to identify potentially malicious activities and software before they can do lasting damage. These produce tremendous amounts of AI cyber data on detected issues or threats.
AI cybersecurity data helps CISOs present a case in front of the board, but often they can only report what risks were mitigated or potential risks raised and not how much was, or could be, saved in financial terms.
Making sense of AI cyber data becomes a challenge in itself because key components to calculate financial impact are missing.
To demonstrate: Risks are ranked on a low, mid, and high scale. How do you quanitfy and explain how much higher the high risk is than the medium one? How do you argue why some risks are medium instead of high?
Organisations must know figures because they help them decide which risks must be addressed first, and help reduce the uncertainty when choosing risk mitigation solutions.
Industry-wide data provides just a ballpark figure and isn’t accurate enough.
CISOs must transform AI cybersecurity data into information the board will understand and know how to work with—this means using actual numbers and financial impact on their organisation.
The technical data they get from AI solutions is a good start, but they must include regulatory impact and also check and validate the data from AI tools before they go to the board. This is the only way to paint a complete and accurate picture.
Instead of presenting industry events that happened or rely on past incidents, they can use tools that convert AI cyber data from their cyber solutions into actual numbers for security events related to their organisation.
The right tools help them transform the data to financial terms that the executives will understand. This way, they will have an easier time getting approval for cybersecurity investments and defending their risk management decisions.
More importantly, CISOs must make time to check these numbers regularly as it helps create benchmarks that are based on their data instead of wider industry data, providing the most accurate data points for decision-makers to work with.
The changing nature of the cybersecurity environment and the regulatory framework requires frequent security posture analysis and fine-tuning areas with lacking results. This is only possible with using AI cybersecurity data related to your specific organisation and quantifying it.
Boardish helps you get back control over AI cyber data by quantifying and validating all data before you bring it to the board.
Explain it in terms they understand, speak Boardish.