The Key To Stronger Security For CISO Leaders

Sivan Tehila, CEO & Founder of Onyxia Cyber and Cybersecurity Masters Program Director at the YU Katz School of Science and Health.

For years, cybersecurity has been reactive in practice—with organizations only able to take action after a threat or attack is detected. Yet, cyberattacks have major ramifications—from significant financial loss and reputational damage to compliance violations and disruption of operations. In 2025, cybercrime is expected to cost the world $10.5 trillion in total damages.

But what if we could help organizations prevent these attacks and their devastating outcomes with information that lies at our fingertips? As cybersecurity leaders, we will always have to be on guard and aware of emerging external threats, but the key to effective cyber defense may actually lie in the data within, especially as new advances in machine learning support so many actionable and critical insights.

The Devil Is In The Data Details

As Carly Fiorina, former CEO of Hewlett-Packard, shared with theHRDIRECTOR, “The goal is to turn data into information, and information into insight.” For many CISOs and security leaders, the issue is not that the data doesn’t exist but that it isn’t standardized and accessible in order to make informed decisions.

In Onyxia’s Regulations, Reporting, and Risk Management: Voice of the CISO report, we found that 84% of CISOs currently measure the effectiveness and performance of their security programs with either spreadsheets, analysts or a combination of the two approaches. Yet the issue with manual data collection is that it not only takes valuable time away from the security team but can only deliver point-in-time data. In contrast, the cybersecurity landscape is dynamic and in constant motion.

SIEM (security information and event management) technology was introduced in 2005 to centralize and correlate security data from various sources across an organization’s IT environment. But unfortunately, the technology has not lived up to its promise. Many security leaders cite this technology as being clunky and outdated; it’s challenging to integrate and provides an overload of unstructured data that doesn’t address security leaders’ strategic needs. The data exists but the insights from the information it yields do not.

Machine Learning Could Be the Answer

We have officially entered the age of artificial intelligence and, yes, advances in LLM sand chatbots are a core part of this, but so are innovations in another domain: machine learning.

While AI focuses on a machine’s ability to reason, problem-solve or act like a human, machine learning is the subset of AI that processes data to identify patterns and leverages data to improve performance and inform decision-making.

Machine learning can be a powerful force in many industries, and cybersecurity is no exception. Even within cybersecurity, the flexibility of the data and use cases for machine learning in driving better cybersecurity outcomes is limitless.

I see machine learning being most impactful for cybersecurity in three primary areas:

1. Standardization Of Disparate Data: The enterprise CISO typically manages between 60 and 75 security tools. Each of these solutions has its own metrics and systems for data measurement, making standardization of data a daunting task. Machine learning can help with data normalization by identifying patterns and relationships across different datasets, harmonizing the data from various sources, and making it more consistent and easier to analyze.

2. Actionable Insights To Optimize Security Program Performance: Sifting through volumes of data to find the most important takeaways can be a time-consuming task. Machine learning can automate this by uncovering hidden patterns and trends within data, enabling organizations to make informed decisions. In analyzing large datasets, ML algorithms can identify correlations, anomalies and potential opportunities to optimize processes and improve cybersecurity program performance across various domains.

3. Predictive Security Analytics: One of the most exciting applications for machine learning in cybersecurity is predictive analytics. Machine learning algorithms can analyze vast amounts of security data from the past and use this information to forecast security performance in the future. This can help security leaders proactively reduce risk and predictively avoid potential crises.

It’s Our Turn To Benefit From A Data-Driven Approach

Many executive roles have traditionally been able to rely on the power of data to drive better business decisions. Chief financial officers leverage data for real-time data visualization and financial forecasting and modeling, while chief revenue officers harness data to measure campaign performance and project revenue targets.

Now it’s time for innovation in data and machine learning to empower security leaders, especially as new compliance regulations, like the SEC cybersecurity disclosure rules, require precise risk management reporting. Lack of compliance carries heavy consequences, including personal liability and fines of millions of dollars.

As security leaders, we can’t afford to be in the dark when it comes to the health of our cybersecurity programs. Getting the right information and the right insights from our security ecosystem data may just be the ticket to shining a light on stronger cybersecurity outcomes.

Making The Most Of Machine Learning

Starting with AI and machine learning in security demands careful handling due to the sensitivity of the data involved. Balancing privacy and accuracy is crucial; using a third-party platform risks data exposure, while relying solely on mock data may require extended testing for validation. In security applications, training on real-world datasets whenever feasible yields superior results.

Transparency will be very important in the development and post-development of your models. Being able to answer on how you are implementing AI, how you are using the data and what data you are collecting will establish trust with your users.

The Future

As security leaders, we can’t afford to be in the dark when it comes to the health of our cybersecurity programs. Getting the right information and the right insights from our security ecosystem data may just be the ticket to shining a light on stronger cybersecurity outcomes.

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