How to Keep Up With Never-Ending SOC Continuous Improvement
SOC continuous improvement fails when insights get buried in closed tickets. Learn a 6-step LLM pipeline that turns investigation notes into action.
I was there, I sat in every SOC seat out there…
A SOC analyst grinding through alert queues at 2am. Part of an Incident Response team leading running war rooms. A SOC manager in Monday morning stand-ups asking what we learned this week while staring at blank faces.
Every single role. Every single day. And the one thing that never changed across any of them?
The insights, recommendations, self improvement, the de-facto SOC continuous improvement action items were disappearing. Seating documented in a case log for no one to action upon, trapped inside closed tickets that live in a backlog nobody rarely reopens.
I know the why and I feel the overwhelming operations, which is why I’m offering a practical solution for how to continuously improve your SOC with the valuable insights coming out of your investigations.
The Hidden Goldmine You're Sitting On
Every ticket your team closes tells a story. It's not just that an alert fired, then an analyst investigated and eventually closed. There are powerful signals buried in those notes, whether it's a tool with overly noisy alerts, a gap in your email gateway rules, or the same user clicking a phishing link for the third month in a row.
Your tier 1 all the way to your tier 5 analysts and IR responders are generating intelligence every single shift and with every single incident. They know things and they're writing them down. It's useful information but these notes get buried and never read again.
It's a sad truth... I know because I've been in those weekly SOC meetings, I was running them.
It's not a people problem, rather, it's a system problem.
The Weekly Report Trap
The thing people look to as the standard fix is the weekly report. In theory it's elegant: senior analysts summarize the week, extract the learnings, feed them back into tier 1 runbooks and detection improvements. On paper, it's a proper feedback loop.
In practice, it becomes the task that either gets rushed on Friday afternoon or simply doesn't happen. It's for good reason too! Your senior analysts are already stretched because on top of everything they need to do for their jobs, they're also being asked to synthesize everything in themes. You either get a half-hearted copy-paste of ticket titles, or, more likely, you get nothing.
Teams try rotation where everyone takes a turn on the ferris wheel. But in doing so, you face losing important insights and information, not to mention a lack of consistency.
Now add a follow-the-sun operation to this. APAC closes tickets while EMEA is asleep. EMEA handles incidents while Americas is offline. By the time anyone tries to compile a summary, they're working with fragments. Nobody has the full picture. The patterns that only emerge when you look across all shifts stay invisible.
Wait, Can't AI Can Solve This Pretty Easily?
When capable LLMs became available, I thought this was finally solved. Just feed all the investigation summaries in, ask for a weekly report. Done? Not so fast... here's what actually happened.
First attempt: I gave the best LLM models that money can buy more than 250 investigation summaries and asked for a consolidated report. But what I got back was a mess.
What I saw were recommendations repeated five times just with slightly different wording. Severity assessments that made no sense and my “favorite” recommendations that are not feasible, for example “Tune your EDR machine learning to reduce false positives of macro xlsx files”.
No traceability whatsoever, no way to tie anything back to the original investigation and forget about cross referencing with similar recommendations.
Second attempt: I went deep on prompt engineering. Longer prompts. More detailed. With examples. The results improved marginally, but the ceiling was surprisingly low.
The fundamental issue is that when you dump a large context with complex requirements into a single LLM call, it can't hold everything in working memory. It forgets constraints from earlier in the prompt. It hallucinates connections between unrelated incidents. Severity levels come out inconsistent.
One-shot approaches get you mediocre fast. They don't get you useful.
The Breakthrough: Think Multi-Step, Not Prompt
The shift that changed everything was stopping thinking about this as one task and starting to think about it as a multi-step pipeline.
When an experienced analyst writes a weekly report, they don't try to do it all at once. They read, they group, they prioritize, they write. Multiple steps. Each one is different.
So I built it that way.
The 6-step pipeline
Step 1: Classification
The first step does one thing and one thing only. It extracts and categorizes recommendations from raw investigation summaries. It looks for whatever your analysts call them: Recommendations, Do Better, Action Items, Next Steps. It pulls each one out and assigns it to a category: detection, prevention and process improvements.
No dedupe. No severity. Just extraction, done well.
Step 2: Feasibility Assessment
Now we evaluate each recommendation against practical reality. Can this actually be implemented? Is it a quick win or a multi-quarter project? Does it require resources you don't have?
This is also where web search earns its keep. When a recommendation references a specific product or vendor, the model can look up current best practices, product documentations, tech community discussions and verify the suggested configuration actually exists and is supported. Without this, you get generic, often infeasible advice. With it, you get grounded recommendations.
Make sure to use an LLM model that has web search capability via API calls.
Step 3: Citation Attachment
Before touching deduplication, every recommendation gets linked back to its source investigation. This is non-negotiable for a report anyone will actually act on. When a SOC manager reads and SOC teams attempt recommendation implementation, they need to know which investigations triggered that and value with volume justification to it. Otherwise it's just noise or worse, it might break business operations.
Step 4: Deduplication
Three analysts working three separate investigations but same use case, all recommend the same prevention improvement. Without deduplication, you get three entries saying the same thing with slightly different wording. With it, you get one consolidated recommendation that shows it came from three independent investigations, which is actually a stronger signal.
Citations from all source recommendations get merged. Nothing is lost.
Step 5: Severity Classification
Now, with duplicates consolidated, we can assign severity levels that actually mean something. The model evaluates security impact per your instructions, weights and SOC defined severities for each use case. Not how urgent did the analyst feel when writing this, but what is the actual risk if this doesn't get addressed built on your SOC knowledge base.
Separating this from extraction forces objectivity. If you try to assign severity while also pulling recommendations from raw notes, the analyst's tone bleeds in and skews the assessment.
Step 6: Report Generation
Everything feeds into the final structure. The model has category breakdown, feasibility assessments, severity levels, citation references. It produces a coherent report with an executive summary and recommendations sorted by severity, with enough context to actually act on. Also comparing recommendations week on week to get remediation/implementation progress for repeated action items.
Add another layer of disregard recommendations and you have a magnificent mechanism.
No LLM at this stage, actually. It's programmatic and deterministic. It assigns citation letters for easy grounding and reference of recommendation with feasibility (A, B, C...), builds the reasoning section for each recommendation, and outputs clean JSON ready for whatever you want to do with it.
Why This Architecture Actually Works
The goal is to achieve focused context at each step. Instead of one massive prompt juggling ten objectives, each step gets only what it needs. Fewer constraints to forget.
Modular iteration is the name of the game here. When severity ratings were inconsistent, I refined only the severity prompt. When analysts switched from Recommendations to Do Better as their section header, I updated only the classification step and nothing else broke.
Inspectable intermediate outputs. Between every step, results are saved. If something looks wrong in the final report, you can trace back through the pipeline and find exactly where it broke. Debugging is possible, which is not nothing.
Web search in the right place. Not as a general capability, but specifically in the feasibility step where it does the most work. Validating that a recommended configuration actually exists changes the quality of the output completely.
The Payoff
Your analysts don't change anything, they can run the same investigations, keep the same ticket notes they're already writing. The pipeline simply runs against their existing documentation.
The output is consistent. Same structure, same categories, same severity criteria, every week. You can compare week over week and actually spot trends. You can see if the same recommendations keep surfacing, which means they're not getting actioned, which is itself a signal.
The feedback loop that should have existed closes automatically. Tier 2 findings reach tier 1. Detection gaps surface. The Monday morning question about what we learned has an answer.
Build it or use it
Building this right takes time. Getting prompts tuned for the variety in how analysts write, handling edge cases, making it robust across different ticketing systems. It's not weekend work.
If you want to build it yourself: start with extraction only. Get that reliable first. Then add deduplication. Then severity. Don't try to build the whole thing at once.
If you'd rather not build tooling while also running a SOC, this is exactly what we built at Legion Security. Already tuned across real SOC environments, connected to your existing ticketing system, your analysts change nothing.
Either way: stop burying the intelligence your team generates every day.
Your team is learning constantly. Those lessons deserve to surface.
Written by someone who's been the analyst, the IR lead, and the manager staring at the empty Monday morning whiteboard.
Demand for agentic security that actually works in complex enterprise environments has never been higher, and today we're excited to take a meaningful step forward in meeting it
We're excited to announce that Legion Security has partnered with Optiv to become an Authorized Partner to help enterprises stop talking about the same-old-problem, and start putting AI to work. Security teams are under pressure that doesn't need a lot of explaining. Analysts, engineers, and practitioners are being asked to do more with less; more alerts, more tools, more threat surface, and fewer people to manage it all. AI was supposed to be the great equalizer, and the promise of the AI SOC was compelling: automate the noise, free up your people, let machines handle the volume.
The reality has been… more complicated.
Most AI security tools were built generically for a generic security team in a generic enterprise. One problem with this is… what is an average security team? Every large organization has processes that are entirely their own: workflows built around a specific stack, custom tools that were built and tuned over long stretches, tribal knowledge accumulated over years, investigation procedures tuned to their environment, their risk tolerance, their regulators, their customers.
Heavy API integrations try to stitch it together but end up slow, brittle, and context-poor (at best). And agents that operate inside a black box create exactly the kind of trust deficit that makes security leaders hesitate to hand anything off at all.
This is the gap Legion was built to close.
A Different Approach to Agentic Security in the Enterprise
The premise of Legion is straightforward: nobody knows your security operations like you do. Our platform doesn't arrive with assumptions about how your team should work. Instead, it observes and learns from how your team actually works; across your tools, your workflows, your most repetitive processes and your most bespoke ones, and then uses that knowledge to build optimized AI agents that operate within the context of your organization.
We don’t require integrations for full contextual awareness. We’re an open book (no black box) that leans on our browser-based approach to see what your analysts see and do, learns what they know, and earns YOUR trust before taking action.
The result is agentic security that can actually scale in the enterprise — not by replacing how teams work, but by amplifying it.
The Imperative for Partnering with Optiv
Becoming an Optiv Authorized Partner matters because of what Optiv represents to the enterprise security buyer. Optiv works with organizations that have mature, complex security programs; exactly the kind of environment where Legion's approach of learning from bespoke processes is most valuable.
Enterprise security leaders look to trusted advisors to help them evaluate fit, plan implementation, and optimize outcomes over time. Optiv's position in the market as an integrator with deep relationships and deep domain expertise makes them uniquely positioned to bring best-in-breed solutions to the organizations that need it most and to help them get maximum value from it.
This partnership reflects something we're hearing consistently in the market: enterprises want agentic security, but they want it on their terms. They want AI that understands their environment before it acts in it. They want partners who can help them think through where automation should start, how to build confidence in the system over time, and how to expand from their first use cases into a broader program.
That's exactly what this partnership is designed to deliver.
What It Signals More Broadly
The Optiv partnership is a data point in a larger trend. Channel partners; the integrators, MSSPs, and advisors who sit closest to enterprise security buyers, are increasingly being asked about agentic security. Their clients want to know what's real, what's ready, and what actually works in complex environments.
For Legion, this is an important milestone in building the ecosystem that enterprise agentic security requires. We're grateful to the Optiv team for their partnership and excited about what we'll build together. And for enterprise security leaders who have been watching the agentic security space and wondering what a path to trusted AI adoption actually looks like, we'd love to show you.
Interested in learning how Legion Security and Optiv can help your organization automate, scale, and elevate your security posture? Get in touch.
Legion Security is now an Optiv Authorized Partner. Enterprise security teams can now deploy agentic AI for security operations that understands and optimizes agentic workflows without integrations, black boxes, or needing to ask teams to change how they work.
Legion Security is Now Available on Google Cloud Marketplace
Security operations were built around human investigators. Skilled analysts, working manually across dozens of tools, piecing together evidence, making judgment calls, closing cases. But as alert volumes outpaced human capacity, institutional knowledge became a bottleneck, and the complexity of the modern enterprise made scaling impossible. The industry responded with more headcount, more tools, more automation. None of it solved the fundamental problem.
Legion introduces a different operating model entirely.
What Legion Does
Legion observes how your analysts operate when running real investigations, learning your organizational context, tools, past cases, playbooks, runbooks and all other tribal knowledge in order to understand what an optimal investigation looks like for your environment. This is then turned into an easily editable and audible workflow which can be automated when you’re ready. Powered by Google Cloud's Gemini models, each workflow is executed by AI agents that reason through the evidence and provide a verdict and even remediate. This is all accomplished with no manual playbook writing or need to document predefined rules.
But legion goes well beyond workflow creation. As Legion builds trust in its performance, teams can choose to keep a human in the loop to approve every decision or have Legion operate fully autonomously reducing MTTR eliminating MTTA, allowing analysts to focus on more novel investigations that are becoming more and more common in the world of AI.
Memory: The Compounding Advantage
Every investigation Legion conducts makes it smarter. A persistent memory layer continuously captures context from previous cases, your SOC knowledge base, and direct analyst feedback, feeding all of it back into future investigations and decisions. Institutional knowledge that once lived in the heads of your most experienced analysts becomes a permanent, improving organizational asset. The more Legion works, the better it gets. That's not a feature. That's a compounding strategic advantage.
Zero Integrations. Immediate Value.
Most security automation platforms fail at the same hurdle: integrations. Enterprises face months of API work, custom connectors, and professional services before anything runs in production, or are forced to adopt entirely new tools and processes, something most complex enterprises simply can't do.
Legion operates natively in the browser, which means it works across your entire security stack, from threat intel platforms to legacy internal tools, without any API configuration. If your analysts can open it in a browser, Legion can learn from it, generate workflows from it, and execute investigations through it.
Proven Results at Scale
The impact Legion delivers isn't theoretical:
As the head of Security at Virgin Money put it, Legion is “like evolving from handcrafted systems to precision manufacturing aligned to our flow (except) faster, repeatable and secure”.
Legion works with the worlds largest enterprises and delivers strong results:
- A large insurance organization automated 24,000 investigations and cut mean time to respond from 20 minutes to 2 minutes.
- WELL Health Technologies reduced investigation times by 81%, allowing existing analysts to handle significantly higher alert volumes without additional headcount.
- The University of Tulsa cut investigation times in half, enabling their team to overcome capacity limits with the staff they already had.
Across deployments, Legion reduces mean time to investigate by up to 85% and response times by up to 90%.
Built on Google Cloud
Legion's integration with Google Cloud goes deeper than the Marketplace listing. The platform runs on Google Cloud infrastructure and leverages Gemini models to power its AI reasoning, combining Legion's browser-native architecture with Google Cloud's security, scale, and model quality.
For organizations already invested in Google Cloud and Google SecOps, Legion extends that ecosystem directly into the analyst workflow.
Who It's For
Legion is purpose-built for enterprise security operations teams, CISOs, VPs of Information Security, SOC Directors, and Security Operations Managers at organizations running in-house SOCs. If your team is dealing with any of the following, Legion was built for you:
- Alert volumes that have outpaced your team's capacity
- Analyst burnout from manual, repetitive investigation work
- Institutional knowledge that walks out the door when senior analysts do
- Automation gaps caused by complex integration requirements
Available Now on Google Cloud Marketplace
Legion Security is available today on Google Cloud Marketplace, allowing customers to apply their spend toward their annual Google contract and simplify procurement. For security teams ready to move beyond the limits of traditional operations, this is where that transformation begins.
Legion is officially on the Google Cloud Marketplace.
Introduction
When automating modern security investigations with LLM-based agents, to conduct multi-step security investigations, a typical workflow begins with an alert - say, a reported phishing email - and the agent iteratively queries tools such as Microsoft Defender Threat Explorer, Splunk, or CrowdStrike to gather evidence, assess scope, and recommend containment actions.
At each step, the agent receives query results containing raw IOCs: sender addresses, embedded URLs, source IPs, recipient domains, and device hostnames. It must reason about these indicators, decide whether to refine its search or conclude the investigation, and return its findings as structured output.
This works well for short investigations. But as the number of steps grows, an issue emerges: the agent's context window fills with repeated, verbose indicator values, the model begins echoing raw IOCs inconsistently, and the structured outputs it produces become increasingly fragile.
[Figure 1: High-level architecture of an AI-driven investigation agent.]
Consider a phishing investigation that proceeds through four steps:
- Initial query: Search for emails from a reported sender to a specific recipient. The results contain the sender's email address and a handful of URLs.
- Scope expansion: Search for all emails from the same sender across the organization. The results return 22 emails with SharePoint URLs, tracking links, and font-file references.
- URL analysis: Search by specific URLs found in step 2. Additional domains and redirects surface.
- Conclusion: The agent summarizes its findings and lists all relevant IOCs.
By step 4, the agent's prompt contains the full history of steps 1 through 3 - including every raw URL, email address, and domain mentioned in each step's results and the agent's own reasoning. Some of these URLs are long tracking links with base64-encoded parameters, easily exceeding 200 characters each.
This accumulation created three concrete problems.
- Token bloat. Raw IOC values, particularly URLs with tracking parameters and encoded payloads, consumed a disproportionate share of the context window. A single newsletter email might contain 30+ URLs, each repeated in the query results, the agent's reasoning, and the indicators list, tripling the token cost per IOC, per step.
- Over-reporting. When asked to list relevant indicators, the model would frequently dump every IOC it had ever seen into the response - even when the current step involved only one or two. In one case, an agent listed all 145 email addresses from its registry when the current query concerned a single sender.
- Structural fragility. Query results from security tools sometimes contained comma-separated URL lists embedded in strings. When the model attempted to reproduce these in its JSON output, it produced malformed structures - unescaped commas, broken string boundaries, and invalid nesting. In our baseline evaluation, only approximately 80% of model responses parsed as valid JSON.
Our Approach
We addressed these problems with a three-part system:
- A unified IOC manager that extracts and indexes indicators.
- An IOC prompt adjustment that instructs the model on how to use indexed references.
- A preprocessing step that cleans malformed tool output before it reaches the model.
IOC Extraction and Indexing
The core of the system is an IOC manager that maintains a registry of all indicators encountered during an investigation. When new text enters the pipeline - whether from tool query results or from the agent's own prior reasoning - the manager scans it using a set of type-specific patterns covering URLs, email addresses, IPv4 addresses, file hashes, hostnames, and domains.
Each newly discovered IOC is assigned a compact symbolic reference following a consistent naming convention: the first email becomes EMAIL01, the first URL becomes URL01, the second domain becomes DOMAIN02, and so on. The original value is stored in the registry, and all occurrences in the text are replaced with the corresponding reference.
Extraction order matters. URLs are processed first, because a URL contains both a domain and potentially an IP address. By extracting URLs before domains and IPs, we prevent the system from fragmenting a single indicator into multiple overlapping entries.
The manager also performs selective extraction. In a typical prompt, the first section contains static task instructions - tool descriptions, output format specifications, and investigation guidelines. IOC extraction is applied only to the dynamic sections (step history and query results), leaving instruction text unchanged. This prevents false positives from example IOCs embedded in the prompt template.
Deduplication is handled through a value-to-reference mapping. If the same IOC appears in step 1 and again in step 3, it receives the same reference both times, ensuring consistent tracking across the entire investigation.
[Figure 3: The IOC extraction pipeline.]
IOC Prompt Adjustment
Extraction alone is not sufficient. Even when the input prompt uses symbolic references, the model may revert to generating raw IOC values in its output, particularly if the system prompt or prior conversation history contains raw values, or if the model has seen the actual value during context processing.
To address this, we developed an IOC prompt adjustment, a compact structured appendix appended to the user prompt that explicitly instructs the model on how to handle IOCs. The adjustment establishes three rules:
- In reasoning: Always use symbolic references. Never write raw email addresses, URLs, IP addresses, or domains. Instead of writing a raw sender address followed by a description of the campaign, use the corresponding reference identifier throughout.
- In the indicators field: Distinguish between known and new IOCs. For indicators already present in the registry, use the symbolic reference. For indicators being reported for the first time, newly discovered in the current step's results, use the actual value, so it can be added to the registry for subsequent steps.
- Relevance filtering: Only include IOCs that are directly relevant to the current investigation step. Do not copy all registry entries into every response.
The IOC prompt adjustment includes a populated copy of the current IOC registry, mapping each reference to its actual value, so the model can look up identifiers when constructing its reasoning. It also provides correct and incorrect examples, a validation checklist, and explicit rejection criteria.
We tested two versions of the IOC prompt adjustment: a comprehensive version with extensive examples and redundant emphasis, and an optimized version that distills the same rules more concisely. Both achieved equivalent compliance rates, suggesting that clarity of instruction matters more than volume of repetition.
Input Preprocessing
The second component addresses a problem upstream of the model: malformed tool output. Security tool APIs sometimes return URL lists as comma-separated values within a single string field, rather than as properly structured arrays. When passed through to the model as-is, these malformed strings caused structured output generation failures.
Our preprocessing step detects comma-separated URL patterns in query results and reformats them into clean, numbered lists before the text reaches the model. This small transformation, applied before IOC extraction, resolved the structured output validity issue independently of the other components.
Evaluation
Setup
We evaluated the system using 10 real-world investigation traces captured from production. Each trace represents a complete phishing investigation conducted through several security tools, containing the system prompt, user prompt with step history, and the raw query results that the model must reason about.
For each trace, we ran 10 iterations with the same prompt configuration, measuring two metrics:
- JSON validity: Whether the model's response parsed as valid a structured output.
- IOC reference compliance: Whether the response used symbolic references exclusively in its reasoning field (no raw IOC values) and correctly distinguished between known references and new actual values in its indicators field.
We tested four configurations to isolate the contribution of each component.
Results
JSON validity. The baseline configuration produced valid JSON in approximately 80% of responses. Adding URL preprocessing alone brought this to nearly 100%, confirming that malformed tool output - not model capability - was the root cause of parsing failures. All configurations that included the enforcer achieved 100% validity.
IOC compliance. Without the IOC prompt adjustment, the model never spontaneously adopted symbolic references, compliance was 0% regardless of whether the input text had been processed by the IOC manager. With the prompt adjustment, compliance jumped to 100% across all traces and iterations. This held for both the comprehensive and optimized prompt adjustment variants.
Component independence. The results reveal a clean separation of concerns: URL preprocessing fixes JSON validity, the IOC manager fixes IOC compliance, and provides the underlying registry and extraction infrastructure that makes both possible.
Qualitative Observations
Beyond the quantitative metrics, we observed several qualitative improvements:
- Reduced prompt size. Replacing verbose URLs (some exceeding 200 characters) with compact references, meaningfully reduced token consumption in the step history, particularly for investigations involving newsletter or marketing emails with numerous tracking links.
- Consistent cross-step tracking. The registry ensured that the same IOC received the same reference throughout the investigation, this is particularly helpful with IOCs referenced throughout multiple steps in the investigation.
- Focused indicator reporting. With the IOC manager's relevance-filtering instruction, the model stopped dumping entire registries into its responses. Indicator lists became proportional to the current step's scope rather than the investigation's total history.
Discussion
Why Extraction Without the IOC Prompt Adjustment Fails
A natural question is why input-side extraction alone does not work. If the prompt already contains a symbolic reference instead of a raw email address, why does the model still generate raw values in its output?
The answer lies in how LLMs process context. The model has access to the full prompt, including sections where the actual IOC value may still appear, task-specific inputs, quoted alert descriptions, or the registry itself. More fundamentally, the model's training distribution contains overwhelmingly more examples of raw IOC values than of symbolic reference systems. Without explicit instruction, the model defaults to the more familiar pattern.
This finding has a broader implication for LLM-based agent design: transforming the input is necessary but not sufficient when you need the model to adopt a non-default output convention. Explicit behavioral instruction, the IOC prompt adjustment, bridges the gap.
Limitations
Our evaluation has several limitations worth noting. All traces were drawn from a single investigation type (phishing via specific security tools). While the IOC types encountered are representative of broader security operations, there are additional evaluations to be done.
The evaluation was conducted with a single model (chatGPT4.1). Different models may exhibit different compliance characteristics, and the prompt mechanism may need tuning for models with different instruction-following tendencies.
Finally, our compliance metric is binary - a response either uses references correctly or it does not. A more granular metric could capture partial compliance and might reveal subtler performance trends across model versions or investigation complexities.
Conclusion
We presented an IOC indexing system for AI-driven security investigations that addresses three interrelated problems: token bloat from repeated raw indicator values, inconsistent IOC tracking across investigation steps, and structural fragility in model-generated structured outputs.
The system combines automated IOC extraction with symbolic reference assignment, explicit behavioral guidance through prompt engineering, and input preprocessing to handle malformed tool output. Across 100 evaluation runs on 10 production investigation traces, the full system achieved 100% JSON validity and 100% IOC reference compliance, up from approximately 80% and 0% respectively at baseline.
The key insight is that managing IOCs in the context of LLM-based agents requires intervention at both the input and output stages. Extraction and indexing normalize the input, but only explicit prompt-level guidance ensures the model adopts the reference convention in its generated output. Neither component alone is sufficient; together, they eliminate the problem entirely.
As SOC automation platforms handle increasingly complex, multi-step investigations, structured approaches to managing the information that flows through the agent's context window become essential. IOC indexing is one instance of a more general pattern: giving the agent a well-organized working memory that scales with investigation complexity rather than against it.
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When LLMs investigate security alerts autonomously, they encounter dozens, sometimes hundreds of IOCs: emails, URLs, IPs, domains, and hostnames. Left unmanaged, these inflate token costs, produce inconsistent references, and break structured output. Our IOC indexing system replaces raw indicators with compact symbolic references the model reuses throughout its reasoning. Across 100 evaluation runs, it took JSON validity from ~80% to 100% and IOC reference compliance to 100%."
