How Early Detection Capabilities Can Uncover and Protect Against Hidden Threats

Every major cyberattack begins with subtle signs that can be as elusive as a needle in a haystack. The early indicators of a potential issue, or anomaly, within your technology setup can easily be overlooked or disregarded, as these first signals of something amiss may manifest differently across various environments and devices. Traditional cybersecurity techniques are great when the threat is known to the broader community, but what about unknown threats we have yet to discover? The challenge lies in identifying the early indications of potential threats before they escalate into full-blown breaches. This blog will explore how granular early detection capabilities can protect your organization from unseen and unknown attacks, and how AI-based advanced anomaly detection can keep you ahead of the game.

Begin with an Accurate Asset Inventory

There has been a massive expansion in the number of connected assets in any given environment and an increased diversity of asset types, spanning IT, OT, IoT, and IoMT. The vast majority of these connected assets remain unseen, unmanaged, and not fully secured. Traditional security solutions often miss threats within these assets, and without a comprehensive view of everything in your network, cyber attacks can easily exploit these visibility gaps. Deep visibility of your technology infrastructure is foundational to effective protection. Without this, innovative threat detection and artificial intelligence will fall short, essentially serving as window dressing while your doors are left unlocked.

Stop Attacks in Their Tracks Before They Materialize

Constant vigilance for cyber threats is key, but it is easier said than done. In the last six years, the number of disclosed vulnerabilities has exploded, with an increase of 689%. With cyberattacks on the rise and bad actors using increasingly sophisticated tactics, organizations must prioritize early detection mechanisms that can catch threats while they are still forming.

One key method in threat detection and prevention is detecting anomalous behavior and uncovering the first indicators of unknown attacks at their most microscopic level. Imagine if you could adopt a truly proactive approach where, instead of reacting to incidents, you could identify and isolate potential threats within your environment before a specific signature or pattern for that threat is even known or defined. This would allow you to change from incident response and crisis management in the wake of potential threat discovery to more targeted, strategic efforts. Detecting the most minute anomalies in network traffic and behavior minimizes potential damage, reduces recovery costs, and safeguards your organization with precise detection.

The Role of AI in Reducing False Positives

One of the biggest challenges in threat detection is managing false positives, which can overwhelm security teams and lead to alert fatigue. In essence, if everything is flagged as a potential threat, nothing can be prioritized and actioned swiftly. Artificial intelligence (AI) plays a crucial role in minimizing false alarms. By continuously learning from the data, AI can improve detection accuracy, ensuring that alerts are both relevant and actionable. AI tools that provide enhanced asset visibility and behavioral data, such as Armis Centrix™ can also provide additional context about your assets—where they are used, for what purpose, and their importance to business operations—to provide recommendations based on how your environment works, rather than generic estimates. This precision allows security teams to focus on genuine threats, optimizing their response efforts.

The Difference Between Traditional and Advanced Anomaly Detection

Traditional anomaly detection techniques consist of methods such as signature-based approaches, rule-based analysis using predefined heuristics or indicators, or establishing a baseline of behavior that is static and not kept up-to-date over time. Many of these approaches fall short in detecting new attack methodologies and unknown threats, which pose a significant risk as attack techniques continue to evolve.

When evaluating your threat detection and anomaly detection capabilities, here are 8 characteristics and features you should look for:

1. Comprehensive Asset Inventory: Do you have a view of every asset in your environment? Does this include all IT, IoT, and even medical and OT assets? The ability to view and manage everything in one platform is key for enabling complete coverage, faster decision-making, and response.
2. Asset-Centric Behavior Monitoring: Can you establish the normal range of behavior for each asset? A modern approach will take an asset-driven view to provide a consistent behavioral profile over time and enhance asset context.
3. Dynamic Behavioral Baselines: If you know the normal behavior of a device, can you keep that up-to-date over time? Behavioral baselines should be established over a sufficiently long period to include a view of infrequent but normal behaviors. Baselines should evolve to avoid false positives or missed alerts.
4. Timely and Actionable Alerts: If something unexpected appears on your network, how and how fast are you notified? Look for alert capabilities that include full context of what has happened, and the recommended course of action. It should allow you to focus on the most meaningful and prioritized set of alerts and manage risks in real-time.
5. High Level of Accuracy: If you receive alerts, are they accurate? If not, are you viewing too many alerts or not enough? Conventional methods can struggle with balancing false positives and false negatives. Effective anomaly uses contextual factors like source, destination, and traffic volumes across your network. This lets you prioritize the more likely threats targeting high-risk assets or originating from potentially malicious external sources while reducing the noise from benign anomalies.
6. Multiple Detection Models: If something is flagged as an anomaly, can you be certain it represents a genuine threat? Having multiple detection models and methods that consider device-specific baselines and environmental baselines reduces noise as much as possible. This provides aggregated, accurate alerts and ensures that you cut through the noise and enhance your overall security posture instead of being inundated with false positives and non-threatening alerts.
7. Dashboards and Interactive Analytics: How do you continuously monitor for emerging threats? Can you see, at a glance, if threats have been detected, actioned or resolved? Look for visualization capabilities that provide in-depth dashboards and reports. The ability to drill down into granular details of anomalous communications can provide more detail and the context you need to bolster your defenses. Your security platform should work with you, not present a barrier of entry.
8. Automation and AI: Are detection and mitigation wasting precious manual hours? Effective use of automation in tandem with anomaly detection capabilities can streamline the process from detection to mitigation, maximizing your containment efforts. Automated segmentation policies can ensure any threats are contained, preventing further access to your network. Configurable search functions and automated response workflows can streamline remediation without waiting for manual intervention. These capabilities should consider the context of each device and the conditions specific to your environment.
9. Technology Interoperability: Do your technology systems work well and facilitate easy integration? Any new solution should not only provide its own standalone value, but also integrate with your existing security stack to maximize your investment in existing security tools, and therefore fortify your protection.

A key differentiating factor in effective anomaly and threat detection is establishing an effective baseline of behavior. The Armis Asset Intelligence Engine is a giant, crowdsourced, cloud-based knowledge base – the largest in the world– that tracks over five billion assets, with millions more added daily. This allows us to see all devices and immediately understand what they are and how they are being used within their unique environment. This extensive visibility and contextual knowledge of any and every device with Armis Centrix™ allows you to accurately establish a baseline of behavior and keep it up-to-date. Armed with this information, this allows precise identification of any deviations from the baseline for any device, port, or protocol. The combination of AI-based asset intelligence and AI-based anomaly detection reduces the time it takes to investigate and respond to the first indicators of malicious or anomalous activity.

Armis Centrix™ provides an accurate, intelligence-driven approach to aggregated anomaly detection that ensures you are always one step ahead of potential threats. Our cloud-based multi-detection engine leverages policy-based threat detection, anomaly detection, and AI-powered early warning alerts to power best-in-class protection of relevant MITRE Tactics, Techniques, and Procedures (TTPs), including Initial Access, Exfiltration, Command and Control, Collection, Discovery, Lateral Movement, and Impact.

Anomaly detection at its best serves as a crucial line of defense for proactive cyber threat detection. By detecting anomalies early and containing potential threats swiftly, organizations can mitigate the risk of further compromise, data exfiltration, or system disruption, minimizing the overall impact and damage. Time is a precious resource when it comes to cybersecurity. By leveraging innovative technologies, AI-driven insights, and proactive, contextual alerts, security teams can get the upper hand and safeguard their networks against known and unknown threats.

To learn more about the Armis Centrix™ approach to threat detection and anomaly detection, we encourage you to read on in our brochure.