Security Operations Centres (SOCs) receive alerts from an overwhelming number of sources. Which can lead to huge numbers of irrelevant, duplicated, or false-positive security alert noise being generated by these alert sources. However, this is not just a technical issue; this presents a serious business risk. In order to mitigate this issue, it will take more than just adding additional tools for alert generation and processing — the alert generation, analysis, and prioritisation processes must be fundamentally rethought. At Telemetria, we collaborate closely with security and risk leaders on a daily basis who experience the same frustrating reality. In this blog, we will discuss why 70% of security alerts are noise and how AI fixes it.
Why Security Alert Noise Exists?
- We identified five key reasons why there is so much security alert noise based on our experience building AI-based risk intelligence solutions at Telemetria: fragmented security ecosystems, a lack of business context, static detection models, no cross-domain correlation, and human bottlenecks.
- Organisations have many different and disparate tools used for a variety of cybersecurity use cases, including cybersecurity, TPRM, and SCRM. All of these tools generate alerts independently, thus generating duplicate signals, not allowing for a single unified view of risk, and confusing the end user.
- Traditional detection frameworks do not have the proper context to answer critical questions; for example, “Does this alert affect a critical vendor? Is this asset critical to my business? Without a contextual framework, something looks urgent, and it results in more noise than insight.
- Traditional rule-based systems cannot adjust easily to changing attack vectors. They tend to generate excessive amounts of alerts, do not detect subtle attack patterns, and continuously require manual re-tuning.
- Cyber risk does not exist in an isolated server; it includes several aspects and diversified activities in a single server. Risks posed by vendors, vulnerabilities in the supply chain, and internal attacks are linked with one another. However, most systems do not correlate across these domains; this is an area in which Telemetria specifically addresses.
- Even the best analyst can only handle so many alerts per day; this creates an environment of alert fatigue, delayed response time to incidents, and a higher level of risk exposure because of this.
How Telemetria Uses AI to Eliminate Alert Noise?
- We filter through large amounts of alerts on our platform using machine learning, in order to separate signal from noise.
- Telemetria automatically consolidates various systems, alerting systems, Cyber Security alerts, Third Party Risk Management (TPRM) data and Supply Chain Risk Management (SCRM) data (into a single “risk view” across all of these systems)
- We assign risk scores that are dynamic and based on the real-time or historical impact to an organisation, versus just the technical severity of the alerts. This helps prioritize high risk alerts first over lower-risk alerts, and enables us to disapprove lower-risk alerts automatically.
- Telemetria has built AI Models that will automatically learn from historical incidents, analyst decisions and future threats. This helps Telemetria continuously improve its accuracy and reduce the number of false positives over time.
The fact that 70 Percent of security alert noise is not merely a statistic. It’s a call to action by those organisations to change ways of operating sooner than later, or they will always be struggling with their own inefficiencies and the existence (and impacts) of “hidden” risk. For those organisations that utilise/take advantage of AI-enabled platforms such as Telemetria will be able to transform their chaos into clarity and thus drive the future success of their cybersecurity initiatives—success within today’s cybersecurity world is less about being able to see everything; rather, it’s more and more about being able to “understand” what matters most.