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Effective detection engineering requires a careful balance between capturing real threats and minimizing alert fatigue. Without proper controls, excessive alerts can overwhelm security teams, while overly restrictive thresholds may allow threats to go unnoticed. This is where alert thresholding comes in—a method to set predefined limits on alert volume to ensure detections remain effective and manageable.

PantherAIR is an Automated Incident Response capability for Panther SIEM that uses AWS-native services (Lambda, Step Functions, SQS, API Gateway, and S3) to orchestrate security workflows immediately upon threat detection. By automating common or time-critical tasks—such as verifying suspicious activity over Slack or suspending a compromised Okta account—PantherAIR significantly reduces response times, relieves manual burden, and enhances security posture.

In this tutorial, we will walk through a single function demonstrating multiple security weaknesses commonly found in Python 3 code. By removing multiple common security issues, we mitigate the most critical vulnerabilities. Adopt these best practices in your own projects to maintain a robust security posture. Feel free to share this guide with your team, or use it as a checklist during code reviews. Staying vigilant about security from day one can save you from costly incidents in the future!

Our study of 207 schemas from Panther revealed significant redundancies, with some schemas containing over 80% duplicate fields. By addressing these duplications, organizations can streamline their log management processes and optimize storage and processing requirements.

Threat modeling is a critical component in detection engineering, guiding security teams in proactively identifying potential attack vectors and designing effective detection strategies. By creating a structured view of possible threats, organizations can prioritize their detection efforts, focusing on the most likely and impactful adversary tactics, techniques, and procedures (TTPs). This proactive approach not only strengthens defenses but also enhances the accuracy of alerts, reducing false positives and ensuring more reliable threat detection.

In detection engineering, effective triage is key to prioritizing and addressing security threats efficiently. As the second phase in the detection engineering process, triage involves assessing and ranking alerts based on factors like threat severity, alignment, coverage, and active exploit presence. By thoughtfully triaging each alert, detection engineers can focus their resources on the most pressing and impactful threats, optimizing security efforts.

The Detection Engineering Lifecycle begins with gathering detection requirements — a crucial phase influenced by threat intelligence, red team exercises, business security demands, and insights from security operations. A successful requirements gathering phase ensures valuable input from various departments, making detection development more efficient and minimizing time spent tracking down information.

The Detection Development Lifecycle (DDLC) is a DevOps-inspired framework designed to streamline the creation, deployment, and management of detection rules, empowering security professionals to keep pace with evolving threats. By integrating key phases and controls, the DDLC ensures a structured approach to managing detection rules effectively.

Effective log management and collection, crucial for cybersecurity, enhance threat detection, incident analysis, and compliance through a comprehensive approach that includes gathering logs from various sources and managing them via versioned, and scalable systems. Addressing challenges such as data volume and diverse formats, this practice requires innovative tools, skilled personnel, and regular audits, forming the backbone of a robust security framework capable of navigating the complexities of a dynamic cyber environment.

In the high-speed environment that defines much of today's tech industry, the "get it done asap" mindset often overshadows the critical "get it done right" approach. This race against time is frequently fueled by an internal competitive culture, where teams are pitted against each other to deliver results the fastest, a checkbox culture that values the quantity of completed tasks over their quality, or direct pressure from leadership pushing for deliverables within unreasonable timelines.

Lets review a framework for a detection engineering program evaluation. It emphasizes aligning detection goals with business objectives, using key metrics for assessment, and exploring various detection methods from signature-based to AI-driven. The approach champions continuous feedback, technology evaluations, staff training, and stakeholder engagement, all while benchmarking against industry standards and ensuring adaptability to emerging threats.

In the realm of cybersecurity, the phrase "eating the cost" often surfaces during budget discussions or strategic planning meetings. Security leaders, faced with the complex task of safeguarding digital assets while balancing financial constraints, sometimes opt to absorb certain expenses as part of their operational strategy. This decision, while seemingly straightforward, underscores a deeper commitment to resilience and efficiency in the face of ever-evolving cyber threats.

The roots of this framework stem from the one thing that detection engineers must have to operate: logs. By identifying and classifying log sources, we can outline potential attacks that an asset might be exposed to in a shareable model.

In this era dominated by digital technology, prioritizing cybersecurity has never been more critical. Reflecting on my decade of experience, accumulated alongside other cybersecurity experts, I am convinced that the moment has arrived for me to contribute and give back to the community.