Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer or any other entities with which I am affiliated.

If you’ve been following the latest chatter in the cybersecurity community, you’ve probably caught wind of this new wave of AI SOC (ASOC) solutions. It feels like every vendor is slapping “AI” on their product, but underneath the buzzwords, genuinely interesting evolutions are happening. In a space where the stakes grow higher every day, the debate between Copilot and Autonomous SOC solutions is more relevant than ever. Vendors are moving fast, but the distinctions between these approaches can offer unique advantages and challenges for security operations teams. This post, written in collaboration with Srinivas Mantripragada PhD, explores the differences, the pros and cons, and why blending these approaches could usher in the next generation of modern Security Operations Centers (SOCs).

Two Approaches to AI-Powered SOCs

Copilot-Style Solutions :

Copilot-style solutions act as an AI-powered assistant, responding to prompts like "Why did this alert spike at 3 AM?" or "What’s the blast radius of this incident?" They leverage large language models (LLMs) and other AI technologies to assist SOC analysts by providing:

AI-Based Automated Investigations:

These solutions take a more hands-off, “set it and forget it” approach. They often feature prebuilt automation from grouping related alerts and summarising incidents to auto-enriching them with threat intel or relevant data from your environment. Some even handle partial or full response actions based on a defined playbook (e.g., quarantining a suspicious endpoint, blocking an IP, or disabling a compromised user account). If you’ve ever been overwhelmed by thousands of daily alerts, this is a massive time-saver because it handles routine triage and prioritization automatically.

It’s almost like we’re looking at two sides of the same AI coin. Conversely, copilot solutions depend on prompts to give you supercharged analytics. On the other side, autonomous investigation tools run quietly in the background, feeding you insights without you having to lift a finger. Some solutions excel on the investigation side, helping you perform automated searches or hunts in your SIEM/datalake, while others focus on the response phase (like automatically isolating compromised devices).

The question is: Which approach solves the most urgent problems in the SOC? And perhaps more importantly, is there a sweet spot where combining both yields the best results?

The Two Faces of ASOC

1. Copilot Capabilities

Think of copilot solutions as your personal AI sidekick. You give it a prompt (like “Why does this alert keep coming up?” or “Can you hunt for this IOC across my logs?”) and it spits back a well-structured answer or even a recommended next step. This usually focuses on:

• Alert Investigation: Quickly summarize what an alert is about, what triggered it, and why you should care.

• Enrichment / Context: Connecting the dots by pulling in threat intel, associated domains, IP details, and more.

• Blast Radius Insights: Figuring out if that single compromised endpoint just infected half your environment or not.

• Threat Hunting Starter Searches: Helping build the initial queries for SIEM/datalake investigations.

• Understanding Attack Patterns: Mapping TTPs (Tactics, Techniques, Procedures) to known frameworks like MITRE ATT&CK.

• Automation Skeleton: Drafting quick scripts or mini-playbooks so you can take them and run with it.

• Security Q&A: “Which compliance standard covers this?” or “What’s the difference between X and Y threat vectors?”

• Compliance/GRC Tips: Nudging you towards relevant controls or checklists.

• Vulnerability Management: Highlighting potential CVEs or known exploits.

• Alert Summaries: Summarize a flood of alerts in a readable way, so you don’t go cross-eyed with a SIEM meltdown.

Copilot solutions shine at letting you explore freely. You can keep throwing questions at them, pivot to different angles, and treat them like your knowledgeable coworker who doesn’t mind 3 AM Slack messages.

Pros

• Highly flexible: prompts let you explore infinite possibilities.

• Great for learning, pivoting, and brainstorming your next steps.

• Quick summarization and intelligence gathering.

Cons

• You need to know what to ask. If your prompt is vague, the copilot might stare back blankly (or worse, hallucinate).

• The analysis is only as good as the context you feed it (garbage in, garbage out).

2. Automated / Autonomous Investigations

This is the “sit back and let the AI do the heavy lifting” approach. These solutions rely on pre-built playbooks, AI/ML-driven correlation, and decision trees to group alerts, confirm true positives (TP) vs. false positives (FP), and automatically enrich with context without you having to type a single query.

• No Prompt Needed: The AI agent runs in the background, investigating alerts as they pop up.

• Pre-Populated Insights: You open an incident, and boom, the details are already there: who’s the affected user, what’s the impacted system, and recommended remediation steps.

• Faster FP/TP Determination: With so many alerts flying around, the solution helps cut down on the “is this real or is my SIEM messing with me?” question.

• Automated Response: Some solutions even let you automatically isolate hosts, disable user accounts, or block suspicious IP addresses based on confidence levels.

Pros

• Minimal manual effort; ideal for large environments with high alert volumes.

• Ensures consistent, repeatable triage and response workflows.

• Speeds up detection and response, reducing alert fatigue.

Cons

• Limited flexibility for analysts to pivot or explore beyond pre-built paths.

• Heavy reliance on vendor-defined playbooks, which may require customization for unique environments.

• Can hallucinate or produce incomplete results without proper context.

Challenges and Problems These Solutions Aim to Solve

1. Alert Fatigue: Today’s SOC analysts get hammered with thousands of alerts daily. AI solutions (both Copilot and Autonomous) are trying to reduce the noise and triage the important stuff.

2. Resource Constraints: Skilled cybersecurity pros are hard to come by. Automation and AI-driven triage help smaller teams do the work of bigger teams.

3. Speed of Investigation: The sooner you figure out what’s going on, the sooner you can respond (or go home on time for once). AI aims to accelerate that initial investigation phase.

4. Knowledge Gap: Let’s be real; not everyone is an expert in every single threat or new vulnerability. Copilot solutions help with quick knowledge checks and context.

5. Scalability: As environments grow, so do logs and the potential for chaos. Automated investigations can handle that scale more gracefully (in theory!).

Why You Might Want Both

Now, here’s the juicy part: combining these solutions often brings the best of both worlds.

• Autonomous to Filter Noise: Use the autonomous solution to do the heavy lifting—grouping alerts, identifying false positives, doing basic triage. This is your “24/7 vigilant guard” that doesn’t get bored or need coffee breaks.

• Copilot to Deep-Dive: Once you’ve got a subset of a suspicious activity or a gnarly incident, call in the copilot. Now you can ask deeper questions, pivot in weird directions, or craft custom threat hunts.

• More Control Over Remediation: Autonomous solutions can do auto-response, but you might sometimes want a human + copilot approach for final decisions, especially for high-impact responses like “Shut down the entire domain controller, stat!”

In short, having an autonomous baseline plus a copilot layer can be a total game changer. You get speed and completeness with the AI agent’s auto-investigations, plus flexibility and creativity with the copilot’s prompt-based intelligence.

Final Thoughts

Right now, the industry is still trying to figure out the sweet spot between turning AI agents loose to handle it all and letting human analysts do all the heavy lifting. Some organisations trust autonomous AI to run the show and, in many cases, replace or augment MDR (Managed Detection and Response) solutions especially if they need 24/7 detection, triage, and automated response without an entire outsourced team.

On the flip side, Copilot capabilities are turning into handy add-ons for the big platforms we already use SIEM, SOAR, XDR, or hyperautomation solutions. They slot in to help analysts make better decisions, faster, by providing natural language interactions, context, and suggestions without overhauling the existing stack.

Personally, I still think a “human + AI” hybrid approach is the best of both worlds no FOMO, no blind trust in automation, but plenty of speed and efficiency gains.

If you’re evaluating ASOC tools, consider:

1. Do I need full automation for a large environment that’s nonstop? If yes, autonomous AI (in lieu of or alongside MDR) can offload a ton of repetitive triage.

2. Do I want a copilot that integrates with my SIEM/SOAR/XDR to help analysts answer queries, enrich alerts, and speed up investigations?

3. Why not both? They can complement each other nicely autonomous AI doing the grunt work, and copilot guiding you when you need more nuanced insights.

The good news is, these technologies are evolving at breakneck speed. Keep an eye out for new features that blend the two capabilities, especially as large language models and sophisticated AI agents become the new normal in cybersecurity.

If you want to get on a call and have a discussion about security automation, you can book some time here:

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