Making Sense of AI Cybersecurity Data for Enterprises
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?
The Problem with AI Cyber Data
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.
Why It Gets Complicated
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.
- CISOs often use qualitative methods to display cyber risk, but these aren’t an accurate method to rely on in crucial decision making. They lack the means to provide a definitive prioritisation for identified risks.
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?
- When using quantitative methods, CISOs use data and events from industry and sector to determine the risk and prioritise cybersecurity solutions. The numbers they rely on are from high-profile breaches that happened recently, with focus on those that have affected organisations similar in size, technology, and inner organisation. But this method is missing a way to demonstrate the actual economic impact on their organisation.
- AI solutions used to monitor the organisation are often missing key analytical capabilities. While good at detecting issues and mitigating risk, they cannot show how technology, personnel, processes, and internal policies affect the magnitude and event frequency of each risk or point towards broader systemic issues within the organisation’s security posture.
- AI cyber data lacks information on the impact of legal and regulatory changes to the industry. CISOs can only let the executives know that there’s been a change in regulations and that it will be affecting the organisation. Most often, this will require partnering up with the legal team to help with analysis.
How Can CISOs Get Accurate Numbers for Cyber Risk?
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.
Using AI Cyber Data to Create a Full Picture
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.