Bedford tech company expands with an eye on AI
SilverTech CEO Nick Soggu discusses the promise and risks of AI as his Bedford-based agency acquires Paragon, expanding digital experience services and advancing AI-driven innovation.
Ask a senior executive or board member about their experience evaluating investments in cybersecurity solutions. You will undoubtedly hear frustration understanding the return-on-investment of cybersecurity controls. The argument that “bad things will happen” if we do not invest is not exactly an evidence-based position.
These discussions have come center stage with the onslaught of cyberattacks, any of which result in penalties and fines for data privacy violations, and hefty ransomware payments.
Information technology risk is a form of operational risk, defined as losses incurred for inadequate or failed internal processes, people or systems. It differs from other financial risks executives typically asses based on ROI (credit risk, new product development, plant and equipment, etc.). Operational risks do not yield positive returns, and potential losses may be a catastrophic “black swan” event. They are challenging to model, earthquakes are a good example.
Modeling information security risks has typically used qualitative methods, often referred to as “scoring.” The scoring methods are highly subjective and use ordinal rankings (1,2,3,4). The problem is compounded when attempting to perform mathematical operations (such as multiplying a “score” by likelihood and impact) that is not recognized by statisticians or mathematicians as valid.
The excuse of insufficient data, or limited knowledge of exposures to implement quantitative analyses, are without merit. Douglas Hubbard, creator of the Applied Information Economics Method and founder of Hubbard Decision Research, notes “cybersecurity can use the same quantitative language of risk analysis used in other problems,” adding “there are plenty of fields with massive risk, minimal data, and profoundly chaotic actors that are regularly modeled using traditional mathematical methods.”
The Open Group, a global consortium has proposed a risk taxonomy with a common language as a first step necessary to describe elements of IT Risk, and facilitate quantitative analysis; here are some of the elements:
Risk-based cybersecurity investment utilizes quantitative analytic methods. Analytics estimate the statistical probability of a specific type of loss occurring, and the probable loss amount.
Implementing a risk-based approach is not an easy undertaking. The takeaway here is this: setting a strategic goal to move toward quantitative risk analysis is step one vs. attempting an immediate implementation. The process should be understood and embraced before implementation details are considered. This should not impede implementation of basic cybersecurity controls.
Don Guiou is an information risk consultant based in Jaffrey.