AI SecOps: Implementation and Best Practices
Security Operations, or SecOps, is the culmination of individual processes that prevent vulnerabilities and intrusion of risk into sensitive enterprise assets. This is slightly different from the Security Operations Center (SOC) – which is the organizational unit of people that monitors and prevents security incidents.
This distinction is important because SecOps aims to integrate security processes within the operations pipeline, whereas traditional SOCs extricate security away from IT, essentially isolating security processes. This is why modern SOCs often implement SecOps, as a way of balancing threat prevention with dedicated incident response capabilities.
Because SecOps needs to sit alongside everyday IT and OT workflows – and not get in the way – SecOps automation is an essential piece of the strategy. This article looks at how AI SecOps is evolving, use cases for AI in SecOps, and best practices for Implementing AI in SecOps.
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Introduction to AI SecOps
SecOps is an approach that’s gained considerable support within security-conscious organizations. Every organization’s SecOps needs to adapt to the organization’s unique layout of digital assets, infrastructure, and sensitive data – just as the enterprise grows and adapts to market changes over time. Because SecOps integrates security measures throughout the IT operations lifecycle, it also needs to embed security into every stage of development and operations.
To achieve this, the SOC requires continuous, in-depth visibility into the devices, networks, and endpoints of essentially all users – it’s a mind-boggling quantity of data. Part of the reason that SOC teams were traditionally abstracted away from their developer and IT counterparts was to manage all this data. Around the tiers of analysts, SOC teams also required large numbers of tools to extract and group it. Security Information and Event Management (SIEM) tools, Firewalls, and Endpoint Detection and Response (EDR) all helped process this data and turn it into meaningful information.
AI in security operations is now able to ingest security data at the same rate that it’s produced. As a result, Machine Learning – and its newer Generative AI – is responsible for turning SecOps into a continuous process, allowing security operations to keep pace with IT and development changes. Furthermore, as AI-driven platforms provide greater automation options than ever before, SecOps evolution is being pushed toward streamlined tech stacks, reduced complexity, and higher ROI.
Use Cases of AI in SecOps
Threat Discovery with Fewer False Positives
AI models thrive off large datasets: with AI, the quantities of alerts that could once overwhelm a security team can now be ingested, cross-referenced, and used to detect others. This is in drastic contrast to the traditional approach to threat detection – which just piled security tools on top of one another.
This is the situation one US-based financial firm had found itself in: SOC analysts were required to start every security operation by digging through the vast quantities of data attached to each alert. And because the enterprise had multiple security tooling software, they had to manually identify the same alert across each console and individually follow each lead to determine an alert’s validity and potential damage.
Because AI is able to ingest all of the raw log, network, and device data that goes into a tool’s alert trigger, it’s then able to correlate that alert against corresponding actions on the network, device, or account in question. The result is far fewer false alerts – and, in the event of a genuine security incident – AI can place alerts within the context of a wider attack chain.
Automated Incident Response
Playbooks are the cornerstone of automated response capabilities – it allows lean teams like those at the University of Zurich’s IT department to rapidly implement certain monitoring and response capabilities in response to specific alerts. For instance, in the event of an incident affecting a department’s endpoints, the corresponding IT manager is able to be notified.
Automation can allow lean teams to provide 24/7 coverage even if they don’t have the manpower to have on-call analysts all the time. Automation is made accessible through playbooks – which denote exactly what remediation steps the AI tool should make in response to certain alert types and incidents.
Prioritized Alerts and AI-Enabled Threat Detection
Because AI models can be trained off historical attacks – and can hold an up-to-date understanding of an enterprise’s entire stack of assets – they’re able to categorize alerts according to the potential blast radius. This drastically reduces the burden placed on manual SecOps processes that would otherwise demand long, arduous hours of work to establish.
Alert categorization was taking up a great deal of one city government’s time – in this case, each analyst was expected to operate their own security tool. This left significant gaps that complex attack vectors could potentially exploit. AI-assisted triage allowed them to drastically reduce the manual workload demanded from each analyst, allowing an analyst to get to the bottom of an incident within 10 minutes, rather than several days.
However, actually knowing where and how to implement AI into SecOps is often the first hurdle to implementation.
Best Practices for Implementing AI in SecOps
Define Measurable Goals for Your AI deployment
SMART goals make the world go round – and the focus on measurability is key to defining and successfully implementing a new AI tool. To extract the best ROI possible, it’s best to start by identifying which SecOps processes are taking up most of your analysts’ time.
This could be a specific tool – like a SIEM – or a broader metric, like mean time to respond (MTTR). It could be a step in the workflow that analysts or IT staff have to follow after an alert reaches their inbox; the important point is to identify precisely which component is causing the biggest slowdown. This process will build a picture of exactly what role an AI tool will need to fill: if a major pain point revolves around asset discovery, then an AI firewall integration is perhaps not the biggest priority.
It’s also best to start making this a collaborative effort. Involving C-levels and other executive decision-makers is vital to achieving long-lasting change, and they can help IT and security picture the organizational changes required.
Integrate AI into Your Existing Tools and Workflows
AI technologies thrive in data-rich environments – but they need to be able to pull that data from somewhere. Custom integrations can be difficult and time-consuming, so when looking at AI-based solutions, assess their ability to integrate with your current tools. It’s exceedingly rare that an organization will ever have to start from scratch. Sometimes, if your SIEM, EDR, or firewall is already up and running fine – and the slowdowns come from the analysts’ own limited resources – it’s best to complement your SIEM with AI, rather than conduct a replacement.
Within this, don’t forget that AI requires a lot of security data. If you’re building a dataset from scratch, you’ll need to invest in building a robust and resilient data infrastructure, coupled with strict governance protocols. A strong infrastructure requires implementing secure storage solutions, optimizing data processing capabilities, and establishing efficient data transmission systems to support real-time threat detection and response. On the other hand, a third-party product manages all of this data for you – but make sure you trust the provider.
Tune the SecOps Team to Use an AI-Driven System
While the AI tool needs to be flexible, it must make some changes to analysts’ day-to-day work – that’s what it’s there for. The teams impacted need to know what changes this will entail, and how their own workflows should look. Because SecOps already demands comprehensive security operations training, they should already be familiar with policies and procedures frameworks. In the same way, the AI update needs to break processes down into measurable actions and clear guidance.
With that said, consider the skill sets and experience of the current SecOps members – if there’s some newer team members that are still earning their stripes, consider picking AI tooling that’s approachable and walks them through the automated actions or alert processes it did. This allows for them to build their own confidence when tackling threats. Transparency also builds more trust between the human team and the AI analysis engine, while also allowing the AI’s judgment to be fine-tuned over time.
Build playbooks
Playbooks are the foundation of AI security implementation, and while an AI tool may come with some pre-established ones, it’s best practice to build or modify your own, according to the specific use case you need.
As an example, if a team deals with many external email communications, it’s important to build some playbooks to specifically handle the threat of email phishing. In this case, a central AI platform detects the suspicious grammar or metadata of a phishing email, which then triggers its associated playbook. In this case, the playbook automatically isolates the email – or the endpoint itself if there’s evidence of compromise – and then triggers a password reset. A message is sent to the corresponding security admin, who receives all of that information packaged into one alert. The playbooks your AI model needs depend on your organization’s own setup and responsibilities.
Collectively, these AI-driven SecOps best practices ensure a smooth transition to AI-driven SecOps, whilst delivering maximum ROI.
How Stellar Cyber Enhances AI SecOps
Automated Incident Detection
Stellar Cyber removes the reliance on manual threat detection and rule-based threat identification with multiple layers of AI.
The first of these AIs is focused on detection: Stellar Cyber’s security research team creates and trains supervised models using a mix of publicly available and internally generated datasets. Zero-day and unknown threats are detectable via parallel unsupervised machine learning models. These models establish a baseline of network and user behavior over several weeks. Once data signals are ingested, a GraphML-based AI correlates detections and other data signals, automatically linking related data points to assist analysts. It evaluates connection strength between different events by analyzing properties, timing, and behavioral patterns.
Other forms of AI are based on top of these core discovery capabilities. They bring more accessibility and response capabilities to Stellar Cyber-powered organizations.
Make SecOps Accessible
All of an organization’s real-time security data is represented in two major formats: the first in the kill chain located on the dashboard, and the second is via the Copilot.
The XDR Kill Chain dashboard serves as the default homepage for Stellar Cyber, offering a centralized view of overall risk and detected threats. It enables quick assessments by providing drilldowns into active incidents, high-risk assets, and attack tactics. This streamlined approach helps security teams prioritize critical issues, no matter their individual focal points that can then be dug into further.
Copilot AI, on the other hand, is an LLM-based investigator that accelerates analysts’ own threat analysis projects by providing instant responses to queries. This makes it perfect for rapid data retrieval and explanation, integrating the tool further within SecOps projects.
Omni-surface Visibility
Stellar cyber ingests logs and security data via multiple types of sensors. The network and security sensors gather metadata from physical and virtual switches while aggregating logs for comprehensive visibility. Its Deep Packet Inspection (DPI) analyzes payloads at-pace. Server sensors, on the other hand, are able to collect data from Linux and Windows servers, capturing network traffic, commands, processes, files, and application activity. Expect full compatibility from Windows 98 beyond, and Linux distributions like Ubuntu, CoreOS, and Debian.
The platform sits wherever visibility is needed: whether cloud-based, hybrid, or fully on-premises – or tenant-based – Stellar Cyber incorporates data from anywhere.
Advanced Response AI
Stellar Cyber’s response capabilities extend the tool’s integration with existing security tools: rather than simply ingesting data, however, Stellar can take actions automatically via those same tools.
Because Stellar is focused on rapid implementation, it comes shipped with 40 pre-built threat hunting playbooks that cover the entire attack surface—such as Windows login failures, DNS analysis, and Microsoft 365. This makes threat detection and response more accessible, even for teams without deep security expertise.
Stellar Cyber integrates seamlessly with firewalls, endpoint security, identity and access management tools, ticketing systems, and messaging apps to scale security operations. For more advanced orchestration needs, it supports integration with leading SOAR platforms for streamlined and efficient threat response. Stellar Cyber-powered enterprises enjoy granular control over the triggers, conditions, and output of each playbook – allowing them to closely and neatly follow SecOps best practices. Playbooks can be deployed globally or on a per-tenant basis.
Explore Stellar Cyber AI SecOps
Stellar Cyber’s platform simplifies the adoption of AI in SecOps by focusing on rapid implementation. It enables enterprises to achieve more effective, efficient security operations without a long-winded or vendor-blocked implementation process. Its automation capabilities are available out of the box – to explore Stellar Cyber’s environment and capabilities, schedule a demo.
