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If you've been following the blog, this post is a natural continuation of where we left off in AI Agents vs. Automation Playbooks. In this piece we dug into the philosophical split between rigid, rule-based playbooks and adaptive, context-aware agents. TL;DR: playbooks are great at executing instructions, but AI agents can think. 

One of the questions I keep getting, and honestly something I’m neck-deep working on right now, is how do we actually transition from classical playbook automation to an autonomous SOC? And here’s the deal: the biggest challenge isn’t tech. It’s finding the right balance. And when I say balance I don’t mean those high-level "strategic framework" slides with five circles and a fancy acronym (guilty as charged, that’s most of what I post). What I mean is: take those frameworks and actually turn them into real, tactical stuff. Use-case breakdowns, step-by-step flows, actual hands-on implementations. That’s how you move from theory to value.

Like let’s be honest, you’re not going to just swap out your entire phishing playbook with an AI agent and call it a day. It’s not plug-and-play magic. You need to break it down step by step. Map out each stage. Ask: what needs control? What needs judgment?

That’s also where trust comes in. I’ve heard way too many stories where teams replaced a playbook with an AI agent, ran it once, got mixed results (because duh, nothing just works first try in tech), and then immediately wrote it off as immature tech. The vibe is always, "This sucks, let's wait a few more years."

WRONG.

What they missed was the process. If you treat it like a drop-in replacement, you’re setting it up to fail. But if you audit the structure of your playbooks, and slowly infuse agents where it makes sense, you'll get two big wins: 1) gradual, trackable progress, and 2) analyst buy-in, because now they can see where it helps and where human eyes are still needed.

Done right, you buy analysts back the one thing you can’t scale: time. Done poorly, you add noise, risk, and spark a full-on mutiny in the SOC.

This edition is sponsored by Prophet Security

Strategies for Seamless Integration: Start Where It Hurts Most

If you’re thinking “Let’s deploy AI across everything,” congrats, you’ve already failed. Don’t boil the ocean. Start with the ugliest, most soul-crushing tasks. Think:

• Alert triage in noisy, inconsistent, or context-heavy sources like EDR, user-reported phishing, cloud, or identity setups

• Context gathering from 5+ tools for every incident

• Enrichment that analysts always forget to do

Start where the pain is real and measurable. You want fast wins that show the team, “Hey, this isn’t some vendor fantasy. This thing actually helped me go home on time.”

Tactically, plug agents into existing SOAR playbooks. Replace brittle logic with agent steps that can think, adapt, and yes, decide. Like, swap out that 10-step enrichment chain with a single agent that figures out what data it needs and grabs it. The magic is in the context handling. LLM-powered agents aren’t just smarter bash scripts, they adapt to ambiguity. That’s gold in a SOC.

Some good examples here. Enrichment playbooks. They’re so simple, but time consuming. Not sure if you had a similar story but for me these have always been a pain. They are simple to build, you get your IOC type, feed them to 5-10 different tools and structure the output.

These are the playbooks that break quite often (mainly because they rely on so many different integrations, from API changes, to permissioning, to json structure). And if you figured that out, have you tried the ones that run on IM commands? They are even worse, as they rely on user input, just so many random characters or empty spaces for things to go wrong.

So, simple one—let the agent do this. It will handle these enrichment tasks better, plus it has reasoning and can give you a better verdict on what it finds. Super simple, but it saves tons of time. You can even build your own MCP infra for this if you want to experiment: https://mcpmarket.com/server/enrichment

Or if you want to see how vendors are doing it, check out Prophet AI (details in the vendor spotlight section).

Internal Enrichment involves pulling in data from your own systems. You check things like historical provisioning, resource types, and identity data. For example, who did the action, is it an employee or service account, is it normal for them, and where is it happening (asset details, change requests, vuln data)?

External Enrichment is about threat intel. Check if IPs, accounts, or domains match known bad stuff. IP and domain rep, file hashes, sandbox results, actor TTPs. And don’t just rely on atomic indicators, behavior-based detections are the move.

Other good use-cases:

• Forensic Evidence Collection: file samples, memory and network dumps, properly captured for future investigation or legal.

• Blast Radius Determination: scan for similar signs across systems, stop lateral movement.

• Timeline Analysis: piece together what happened, how it moved, and whether the whole alert was even valid. Reinforces feedback loops.

Train the Team, Not Just the Model

Biggest failure I keep seeing? Teams install agents and never train their humans.

Here’s a hard truth: If your SOC team doesn't know how to work with AI, you’ve just created more confusion, not less. This isn’t about AI literacy 101. It’s about:

• Demystify the Tech
  Run a brown-bag: “How LLMs hallucinate, how playbooks fail, how the agent stitches it together.” Transparency breeds confidence.
  

• Teach Prompting & Delegation
  Analysts should treat the agent like a junior teammate: “Run the malware sandbox playbook on host X and summarize the results.” Good prompts yield gold.
  

• Hands-On Labs
  Spin up lab incidents where the agent proposes actions. Analysts must review, accept, or override, then feed back a verdict. That feedback becomes new training data.
  

• Formalise the Role: Agent Supervisor
  Certify at least one analyst per shift to tune guardrails, review logs, and champion improvements. This new specialisation turns “AI will take my job” into “AI made me team lead.”
  

Consider designating “agent champions”, analysts who are paid to play, test, and improve agent behaviors. These folks become your AI pit crew. They’re not just analysts; they’re automation engineers in disguise.

Pitfalls: Expect the Integration to Punch You in the Face

AI agents aren’t silver bullets. They’re landmines and lifesavers, depending on how you use them. And trust me, stuff will go sideways:

• APIs change, integrations break (welcome to SecOps dependency hell)

• Some analysts will straight-up hate them and assume it’s the beginning of job cuts

This isn’t “set it and forget it” territory. You need to build with failure in mind. That means:

• Run shadow-mode first. Let the agent observe, suggest, but not act. Log every move.

• Bake in guardrails: approval workflows, rate limits, version-controlled policy files.

• Feedback loops: let analysts thumbs up/down outputs and feed that back into tuning.

• Simulations: test the wild edge cases before prod. Show both the wins and the oopsies.

Let’s go deeper on the common faceplants and how to patch them:

Opaque Decisions

Fix: Log every single action. "Because the file was tagged malicious by 3/5 engines, I triggered Playbook 42."Review those logs. Weekly. With the team. Transparency = trust.

Runaway Automation

Fix: Rate-limit anything that deletes/quarantines/isolates. Anything critical needs a human to hit the green button.

Data Privacy & Model Leakage

Fix: Self-host the LLM if you can. If not, use a vendor with strong isolation. Mask all PII before sending. Pull in legal from day one so they don’t freak out later.

Integration Sprawl

Fix: Start small. One data source. One playbook. One shift. Expand only when that stack is rock solid.

Cultural Resistance

Fix: Share the wins loud and proud. "Agent X closed 1,200 false positives last week." Make it clear: no one’s getting laid off. People are just leveling up—doing threat hunts, red teaming, building detections.

Bottom line: this isn’t about flawless tech. It’s about building confidence. If the team doesn’t trust the AI, it won’t matter how smart it is.

Change Management: Win the Humans First

AI agents don’t fail because of tech. They fail because you didn’t manage people.

This is about trust, culture, and showing your team that this isn’t a flashy experiment—it’s a partnership.

• Inclusive Design – Get your analysts in the loop early. Let them help pick the use cases, write the guardrails, and tear apart the results. If they build it, they’re more likely to trust it.

• Quick Wins – Brag a little. Celebrate the first phishing alert contained autonomously. Share the Slack thread when the alert flood dropped 70%. One team literally said, “Our analyst on call slept through the night—for the first time this quarter.” That’s not a metric, that’s a vibe.

• Transparent Guardrails – Don’t just say it’s safe. Publish the policies. Show them what’s blocked, where human approvals kick in, and what the agent won’t touch. This kind of psychological safety flips skeptics into believers.

• Iterative Rollout – Use release rings. Start with low-risk endpoints, then move to workstations, then production servers, then crown jewels. Every successful ring gives you more credibility and more buy-in.

• Make It Personal – Let analysts name the agent. (No joke, we had one named “ClippyButSmarter.”) Internal branding helps.

• Pilot Over Platform – Start with a scoped trial, not a 6-month roadmap. Let the results do the selling.

Make it collaborative. Make it transparent. Make it feel like something you’d want to use, not something that’s being forced on you.

Closing Thoughts

Stacking playbooks, co-pilots, and autonomous agents isn’t about picking one winner, it’s about building a team. You create a SOC where machines do what machines do best, speed, scale, and repetition,while humans double down on what we do best: judgment, creativity, and strategy.

1. Playbooks swarm the routine.

2. Co-pilots make sense of messy data and tell the story.

3. Agents decide, coordinate, and escalate.

This is how you scale without burning out your team. With shadow-mode pilots, policy-as-code guardrails, and a solid change-management plan, you can roll out this triad safely, transforming your SOC from reactive firefighting to proactive threat eradication.

Start small. Stack smart. Iterate fast.