Claw vs custom GPTs for threat hunting - which gives better control over cost and false positives?

Let's cut through the marketing haze. When the CISO starts waving "AI SOC" slides from vendors promising autonomous threat hunting, my first instinct is to check the fine print on the API pricing and the volume of garbage alerts it's going to generate. The real question isn't which magical AI is better; it's which one lets you put it in a chokehold when it starts hallucinating or racking up a six-figure AWS bill.

You've got two main paths here: a purpose-built platform like Claw (or any of the other "AI-native" SOC tools), or rolling your own with a framework around OpenAI/Custom GPTs, maybe with some open-source models as a backend. The control levers for cost and false positives are fundamentally different.

**Claw & Co. (The "Integrated" Route)**
* **Cost Control:** You're buying a black box with a per-seat or per-event license. Predictable, maybe, but you're paying for the whole circus—UI, integrations, their model training—even if you only use the tightrope walker. Scaling means renegotiating contracts. Their "AI" is a bundled service; you can't easily swap the LLM for a cheaper one when doing high-volume log summarization.
* **False Positive Control:** You get whatever knobs they expose. Usually a "confidence slider" and some rule-based pre/post-filters. The real problem is you can't see the prompt templates. If their system prompt says "be overly cautious," you're stuck with it. Tuning requires a support ticket, not a config file change.

**Custom GPTs / Framework (The "DIY" Route)**
* **Cost Control:** This is where you can get surgical, but it requires engineering. You architect the pipeline yourself.
* You can route different tasks to different models. Use a fast, cheap model (like `gpt-3.5-turbo`) for initial triage and enrichment, then only fire the expensive `gpt-4` or `claude-3-opus` for the 2% of cases that pass your filters.
* Implement strict token limits and context window management. Pre-process your logs to strip noise before sending them to the LLM.
* Cache common query results (e.g., "what are common IOCs for APT29?").
* Your biggest cost lever is your own code.
* **False Positive Control:** You have absolute, terrifying control. You write the system prompts. You can force chain-of-thought reasoning, demand citations from the source data, and implement structured output (like JSON) to parse results consistently.

Here's a brutally simplified example of a pipeline step you'd never see inside Claw. This is a pre-filter to avoid even calling an LLM for obvious junk.

```python
# This runs BEFORE any LLM call. Cheap regex beats a $10/million-tokens API call.
def pre_filter_alert(raw_alert):
# Filter out known noisy benign events
benign_patterns = [
r"user.*password.*expiry",
r"backup.*job.*succeeded",
r"legacy-scanner.*benign-signature.*id"
]
for pattern in benign_patterns:
if re.search(pattern, raw_alert['description'], re.IGNORECASE):
return None # Kill it here. Cost: $0.00.

# Enrich with internal threat intel DB lookups first
alert['internal_risk_score'] = query_internal_risk_db(alert['source_ip'])
if alert['internal_risk_score'] < 5:
# Send only to cheap model for fast triage
return route_to_model("gpt-3.5-turbo", alert)
else:
# Okay, now spend the big bucks
return route_to_model("gpt-4", alert)
```

The trade-off is obvious. Claw gives you a faster start, less engineering, and a vendor to blame. The custom route gives you granular control, but now *you* own the pipeline's reliability, the model's drift, and the deployment hell. You're not just a SOC analyst anymore; you're an ML ops engineer.

So, which gives *better* control? The custom route, unequivocally. The real question is whether your team has the bandwidth and skill to build and maintain the plumbing, or if you'd rather pay the "ignorance tax" to a vendor and focus elsewhere.

-- old salt