Headline
Threat Detection Software: A Deep Dive
As the threat landscape evolves and multiplies with more advanced attacks than ever, defending against these modern cyber threats is a monumental challenge for almost any organization. Threat detection is about an organization’s ability to accurately identify threats, be it to the network, an endpoint, another asset or an application – including cloud infrastructure and assets. At scale, threat
As the threat landscape evolves and multiplies with more advanced attacks than ever, defending against these modern cyber threats is a monumental challenge for almost any organization.
Threat detection is about an organization’s ability to accurately identify threats, be it to the network, an endpoint, another asset or an application – including cloud infrastructure and assets. At scale, threat detection analyzes the entire security infrastructure to identify malicious activity that could compromise the ecosystem.
Countless solutions support threat detection, but the key is to have as much data as possible available to bolster your security visibility. If you don’t know what is happening on your systems, threat detection is impossible.
Deploying the right security software is critical for protecting you from threats.
****What do we mean by threat detection software?****
In the early days of threat detection, software was deployed to protect against different forms of malware. However, threat detection has evolved into a much more comprehensive category.
Modern threat detection software addresses the challenges of identifying threats, finding the legitimate alerts out of all the noise, and locating bad actors by using Indicators of Compromise (IoCs).
Today’s threat detection software works across the entire security stack to give security teams the visibility they need to take appropriate steps and actions.
****What capabilities should threat detection software include?****
To meet the demands of a rapidly-changing workplace, good threat detection software should be the cornerstone of a robust threat detection program that includes detection technology for security events, network events and endpoint events.
For security events, data should be aggregated from activity across the network, including access, authentication, and critical system logs. For network events, it’s about identifying traffic patterns and monitoring traffic between and within both trusted networks and the internet. For endpoints, threat detection technology should provide details regarding potentially malicious events on user machines and gather any forensic information to assist in threat investigation.
Ultimately, robust threat detection solutions give security teams the ability to write detections to look for events and patterns of activity that could be indicative of malicious behavior. Security teams often include detection engineers responsible for creating, testing and tuning detections to alert the team of malicious activity, and minimize false positives.
Detection engineering has been evolving to adopt workflows and best practices from software development to help security teams build scalable processes for writing and hardening detections. The term “Detection as Code” has emerged to describe this practice. By treating detections as well-written code that can be tested, checked into source control, and code-reviewed by peers, teams get higher-quality alerts – reducing fatigue and quickly flagging suspicious activity.
Whether it’s an XDR platform, a next-gen SIEM or an IDS, the platform should provide security teams with the ability to craft highly customizable detections, a built-in testing framework, and the ability to adopt a standardized CI/CD workflow
****The traditional software vs SaaS debate for threat detection****
While traditional software and SaaS may both provide the same "software", the approach is drastically different.
The traditional approach would be to install a piece of software and run it locally. However, this has several drawbacks — including high maintenance costs, lack of scalability, and security risks.
By contrast, many SaaS services will automatically update themselves when new versions become available. Plus, you typically get more reliable performance and service levels from vendors.
****The threat detection benefits of cloud-native SaaS****
Traditional security teams may have been slower to embrace cloud native SaaS solutions, as they are typically more understaffed than their general IT counterparts.
Often, the focus on on-prem infrastructure & applications is the result of business leaders operating under the false assumption that their SaaS vendors are responsible for security.
But as their infrastructure becomes even more cloud-based, deploying a SaaS solution is the more practical strategy today and into the future.
We discussed benefits like lower costs and enhanced business agility above, but for security teams, the most crucial advantage is faster detection and remediation.
When new threats and bad actors seem to surface every day, an organization’s security environment needs room for rapid innovation. With serverless technology, security teams can take advantage of scalability, performance and the ability to analyze massive amounts of data quickly.
Most importantly, cloud-native SaaS allows organizations to be proactive about threat detection and management. Modern SaaS security solutions typically include well-honed processes, tracking, and a single pane of glass visibility in a centralized hub for proactive and responsive threat management.
With a swelling tide of security-relevant data that security teams need to collect and analyze to detect threats, traditional tools are not cut out to handle these workloads.
These solutions take threat detection software to new heights with well-honed processes, tracking, and a single pane of glass visibility in a centralized hub for proactive and responsive threat management.
****Panther’s** **cloud-native threat detection software****
With Panther’s serverless approach to threat detection and response, your security team can detect threats in real-time by analyzing logs as they are ingested, giving you the fastest possible time to detection. You’ll also gain the ability to craft high-fidelity detections in Python and leverage standard CI/CD workflows for creating, testing, and updating detections.
It’s easy to write detection rules in Panther. But if you want to get an even better understanding of how you can improve detection efficacy with Panther, book a demo today.
Follow Panther on Twitter and LinkedIn.
Found this article interesting? Follow THN on Facebook, Twitter and LinkedIn to read more exclusive content we post.