Recently, there’s been tremendous hype around artificial intelligence. For cybersecurity practitioners, it’s becoming increasingly important in developing a cybersecurity strategy. AI’s ability to scan millions of events in seconds while identifying anomalies and suspicious patterns is far superior to rule-based scans. It has applications in endpoint protection, securing IoT devices, cloud security, IIoT, and network security – the list goes on.
AI is used to detect malware and prevent data breaches. For example, Google has used it to stop over 100 million spam messages from reaching Gmail and Google Workspace email accounts. Many of these messages would be undetectable using other, traditional methods.
While it’s easy to lump all AI solutions together in one large AI category, doing so would be a mistake. A natural language AI, like the popular ChatGPT, is excellent at creating content and answering questions, but it’s not designed to handle cybersecurity defense plans.
What is Causal AI?
To understand causal AI, it’s instructive, to begin with correlations. Correlations are two events that happen together but are not necessarily caused by one another. For example, someone with long hair uses more shampoo, but using more shampoo won’t cause hair to grow faster. Those items are correlations. You couldn’t learn anything about hair growth by studying shampoo usage.
Correlations are often used in AI predictions, but those predictions are limited. In an article published last summer, Gartner researcher Leinar Ramos noted that correlation-based AI predicts outcomes using statistical relationships, but it cannot predict how different actions would affect the outcome.
In contrast, Causal AI looks at the root causes behind the relationship between two items. It goes beneath the surface to understand the cause-effect relationship. In a causal relationship, Ramos notes that we can only say A causes B when a change to A would change B in some manner.
In the world of AI, insights pulled from correlation-based AI are limited. For insights to remain valid, the processes used to generate the data must always remain the same. Causal insights, however, are more robust and capable of estimating the effect of actions on results.
In a constantly evolving cybersecurity environment dealing with the threat of nation-state-grade attacks, causal AI provides a deeper understanding of the relationships between events and helps security personnel make informed decisions quickly.
Benefits of Causal AI
Causal AI provides security teams with a better understanding of events leading up to a cyberattack. This helps businesses clarify what is needed to reduce the risks of attacks succeeding. Its ability to identify the root causes makes its predictions more reliable. The improved accuracy of these predictions helps drive better decision-making and ultimately, more reliable results.
It also enables organizations to understand the relationship between different events better and see how they contribute to cyber threats. This improved understanding of the cause-and-effect relationship improves operational readiness for cyberattacks.
Through causal AI, security teams can quickly identify incidents and threats that might impact their organization. They can respond rapidly and proactively to any evolving threat, reducing their risk level and improving the likelihood of fending off the attack. It also ensures they can better prioritize cyber risks.
This enhanced predictive visibility enables organizations to allocate their limited cybersecurity resources better. They can recognize the underlying issues contributing to a future attack and prevent the attack from infiltrating their digital perimeter.
Our strategic cyber defense platform, SAGE, uses causal AI to create a context map of everything that matters when defending your security perimeter. It factors in risks, vulnerabilities, assets, and cyber threats as it analyzes how these impact business processes. For example, before opening a new company location in another geographical area, CISOs can work in SAGE to better understand the context of all the data relating to cybersecurity for the new area and pertaining to the industry benchmarks and take actions to best protect the company assets in the process.
SAGE uses natural language processing (NLP), AI technology that understands text and spoken words. This capability can yield far more intelligence than other AI systems, allowing it to deliver superior insights and analyses.
Causal AI in High-Risk Cybersecurity Situations
As mentioned earlier, causal AI’s ability to identify and understand the cause-and-effect relationship puts it in a class above AI tools that look for correlations and patterns. It goes beyond identifying simple associations between events or variables.
This type of AI does require more rigorous experimentation and statistical analysis than other types of AI, but the quality of its predictions significantly reduces the risk of nation-state-grade attacks.
Causal AI is ideally suited for high-risk cybersecurity situations where the underlying causes of the attack are essential to understand. In these scenarios, understanding how events impact one another makes a difference in the way companies prepare themselves for an attack.
Correlation AI, in contrast, is far better suited for identifying patterns, such as reading metadata from IoT events and picking out anomalies indicating a breach. In these circumstances, security teams try to detect an outcome rather than understand how to improve their cyber defense against an attack.
How Does Doing X Affect Y in My Cyber Defense Plan?
The value of AI should be measured in your ability to make smarter decisions. Organizations should be able to ask their AI tools questions about their strategic cyber defense plan, like “Which parts should be added to the cyber defense plan if we increase the SOC budget by 5% next quarter?” or “What must I change in the cyber defense plan, if we purchase company A? “
Causal AI can answer these types of questions and guide your security team as it prioritizes risks and projects. It can protect an organization from spending millions on technologies and projects for no reason. It optimizes cyber defense spending by focusing on the projects and goals that matter most at any given time.
Causal AI empowers the security team to make optimal decisions based on facts, data, and best practices rather than reacting to alerts caused by correlative anomaly detection. It shifts strategic decision-making to a predictive approach, to attend to what is important, rather than continuously focusing on emergencies in an endless “whack-a-mole” game. As a result, it creates a more robust and proactive security stack.
An Essential Element in Your Strategic Cybersecurity Stack
Causal AI gets inside the cause-and-effect relationship and can better predict future cyberattacks, attack vectors, applicable vulnerabilities, etc. Its insights are unparalleled in developing a cyber defense strategy and should be central to your strategic cyber defense planning software in 2023 (and beyond).
