Introducing DragonClaw: The Orchestration Layer For Agents That Knows How Security Teams Actually Work
DragonClaw is Legion's agent orchestration layer for the SOC; grounded in your org's own context, not a generic chatbot bolted onto security tools.
TL:DR: Ask any security team what would give them back the most time, and the answers tend to converge on the same theme: less time spent stitching things together, more time spent actually deciding. These are exactly the things that DragonClaw is built to optimize, as the orchestration layer that deploys Legion’s trusted AI agents into any security task.
Automated workflows have already gotten teams part of the way there, triggering playbooks and kicking off investigations the moment an alert fires. DragonClaw is built upon the foundation of Legion’s platform, in that we require zero integrations in exchange for the ability to operate any tool, and goes further: it leverages the business context (past cases, runbooks, recordings, etc.) to orchestrate the agents needed to respond to an alert or escalation, to tell you why the last three cases like this one got closed the way they did, and to surface the exact query that finds the right evidence in your specific environment. That's the difference between automation that runs a process and intelligence that understands one.
Instead of an analyst hunting across five tools to reconstruct context that already exists somewhere in the organization's own history, DragonClaw brings that context directly to them and performs a task, in their own way, the moment they need it. Ask a question, get a grounded answer or a completed action, drawn from how your organization actually operates, not a generic playbook applied from outside.
The result is analysts can spend more of their time on the judgment calls only a person can make while orchestrating the agentic layer, where DragonClaw handles the reconstruction, the pattern-matching, and the acceleration and scale that used to eat the hours in between.
From Analyst to CISO: Closing the Context Gap in Security Operations
For security analysts, think of real-world threat hunting. Today, it means pulling and reading vast amounts of data across a bunch of different tools before you can even form an opinion or a lead on where to go. DragonClaw runs that process, end-to-end, with agents. DragonClaw consumes data across all of your tools, correlates it, and comes back with a thesis for the analyst to either approve or disapprove.
If you're a CISO or security leader, quickly investigating what the risk or impact is for a CVE requires organizational context not contained in a single tool. DragonClaw assembles all of that data and surfaces the answers, with recommendations, and where appropriate, autonomous actions that can put the findings to work.
Add it up across a team, and the opportunity is real: practitioners who spend their time on judgment instead of relearning tools, leaders with a straight answer whenever they need one, and a security program built to scale with the threat landscape instead of falling further behind it.
Introducing DragonClaw
DragonClaw is Legion Security's agent orchestration layer for the SOC. It gives security teams the ability to invoke Legion's agents in plain conversational language, enabling security teams to seamlessly get work done, or to answer questions about how their processes, tools, and people are actually making decisions.
One thing to be clear is that this is not (yet another) bolt-on chat interface. DragonClaw is the next step in the Legion platform, built on everything Legion has already learned across the tools, knowledge, and decision logic for your team’s security workflows. DragonClaw takes that further, putting that context and institutional knowledge to work answering questions and completing tasks the moment someone asks.
Under the hood, DragonClaw interprets intent, figures out which agents a request actually requires, and orchestrates them; across all tools in the stack, including agents that take real action, like API calls or web interactions, without any integrations required. All of it runs inside configurable guardrails: explicit permission before any response action, only approved tools, and credentials pulled from secure vaults. Nothing about “conversational” means “unsupervised.”
What Changes For Each of You
Threats are scaling with AI. Automation and agents close a large part of that gap, and they'll take a SOC further than headcount ever could… but not all the way. Security teams need humans to stay in the loop, not to keep pace with volume (which they can’t), but to supervise the work, evaluate outcomes, test and challenge what the agents conclude, and make sure security stays something that enables the business rather than something that slows it down or breaks it. Security analysts and leaders serve essentially as the maestros of the agentic orchestra. That's the same place the sharpest thinking on AI lands more broadly: the machine executes and reasons whereas the human owns judgment where needed and accountability.
DragonClaw is what supercharges the security workers. It's what lets a security team orchestrate its agents instead of losing control over what they do. For security practitioners and SOC analysts, that shows up as a partner inside the investigation itself: context and enrichment on demand, memory across past cases, guidance on what to do next, and the ability to generate the right query for your environment instead of learning a new query language from scratch.
For managers and security leadership, it's one place to ask about real-time SLA risk, process improvement opportunities, MTTR and false-positive trends, bottlenecks, coverage gaps, and team workload — instead of stitching the answer together from five dashboards.
For CISOs, DragonClaw provides direct answers on risk posture, SLA exposure, MTTR trends, exposure to a new CVE, audit evidence, automation ROI, and board-ready reporting, available the moment you need them instead of on the next reporting cycle.
Not Another Chatbot, An Orchestrator
Chat interfaces are becoming table stakes across the industry, and we're not going to pretend otherwise; it’s been proven that chat alone isn't a durable differentiator. What makes DragonClaw different is what's underneath it: every answer and every action is grounded in the workflows, case history, and coverage data Legion has already built for your specific security team and your specific organization.
A generic assistant sitting outside your platform can talk about security in general. DragonClaw can talk about your security workflows, because it already has the record of how your security team works.
That's the same principle behind everything Legion builds: AI for defenders should understand how a specific business operates, across its tools, its workflows, its people, before it's trusted to answer questions or take action with real business impact. DragonClaw is where that understanding becomes something every person in your organization can talk to directly, whether that's the analyst mid-investigation, the manager reviewing the week, or the CISO prepping for the board.
DragonClaw will be showcased at Black Hat USA 2026, visit us at Booth #5150 to see it in action!
Security teams don't get to choose their threat model. They inherit it: alert volumes outpacing headcount, attackers moving at machine speed, and a stack of tools that all assume a human is sitting in the middle of every decision. Legion Security was built to change that operating model. It should go without saying that the platform doing the changing has to be secure to its core.
CISA's Secure by Design Pledge exists to push the whole industry toward that standard: security as a default, not an upsell. Legion Security has signed the pledge, and this page lays out how we deliver on each of its seven goals today, where we go beyond them, and where we're still pushing.
One note before diving in. Legion Security is independently certified against SOC 2 Type 2, ISO/IEC 27001:2022, and HIPAA, and against ISO/IEC 42001:2023 for responsible AI management, because AI reasoning sits at the center of everything the platform does and we believe that deserves its own standard of scrutiny. Everything below is backed by audited evidence, not just a blog post. Every report, policy, and pentest result is available on request through our Trust Center.
Authentication
The pledge commitment: Demonstrate actions taken to measurably increase the use of multi-factor authentication across the manufacturer's products
Every Legion Security customer gets enterprise-grade identity protection from day one, not as an add-on. The platform integrates with any SAML 2.0 or OIDC-compliant identity provider, including Okta, Microsoft Entra ID, and Google Workspace, so your existing MFA and conditional access policies extend automatically to every login. No extra licensing tier, no SSO tax, no “contact sales” for basic protection.
For teams still mid-rollout on SSO, direct logins to Legion Security require MFA. There is no opt-out, not for admins, not for anyone.
And because Legion Security operates natively in the browser rather than through a sprawl of API integrations, there are no long-lived API keys quietly wiring it into the rest of your stack. Your analysts sign in once, through the identity layer they already trust, and the platform meets them there. Sessions are scoped and expire like any other corporate login, which is exactly how it should be.
Default Passwords
The pledge commitment: Demonstrate measurable progress towards reducing default passwords across the manufacturer's products.
Default and shared passwords are how breaches happen quietly. Legion Security doesn't have any, and we never will. Every customer environment is provisioned with unique, securely generated credentials from the moment it's stood up, and service-to-service connections run on scoped, short-lived access that expires on its own rather than a static key someone forgot to rotate. There's no fallback password lurking in a setup guide for an attacker to find.
Reducing Entire Classes of Vulnerabilities
The pledge commitment: Demonstrate actions taken towards enabling a significant measurable reduction in the prevalence of one or more vulnerability classes across the manufacturer's products.
Most security automation platforms expand your attack surface before they ever deliver value: months of custom connectors and API plumbing, each one a new door into your environment. Legion Security was built to eliminate that door entirely. The platform works through the browser your analysts already use, so there's no integration sprawl and no expanded blast radius when something goes wrong elsewhere in your stack.
For an AI-native platform, though, the honest conversation starts with a different vulnerability class: prompt injection. Legion Security's agents reason over evidence pulled from your tools, and some of that evidence, phishing emails and attacker-controlled artifacts among it, is adversarial. So we treat all of it as untrusted input. Evidence is validated and normalized before it ever reaches a model, agents operate with tightly scoped permissions rather than broad tenant access, and actions that change your environment pass through explicit approval gates. Malicious content can try to talk to our agents. It doesn't get to instruct them.
When we do find a weakness, whether through the independent penetration tests we commission, a researcher's report, or our own review, we don't stop at the fix. Significant findings get a root cause analysis and a set of preventative changes aimed at the pattern, not just the instance. Security isn't a checkpoint at the end of our development cycle; it's a constraint we design around from the start.
Security Patches
The pledge commitment: Demonstrate actions taken to measurably increase the installation of security patches by customers.
Legion Security runs as SaaS, full stop. There's no patch cycle for your team to manage, no fleet of installations slowly drifting out of date, no tenant running last year's fixes because nobody got around to the upgrade. We ship continuously through automated pipelines, so when we close a gap, every customer is protected at the same moment. The single biggest reason security patches don't get applied, the burden falling on the customer, simply doesn't exist in our model.
Vulnerability Disclosure Policy
The pledge commitment: Publish a vulnerability disclosure policy that authorizes testing in good faith, commits to not pursuing legal action against good-faith researchers, and provides a clear channel to report vulnerabilities.
As a SaaS-only platform, Legion Security maintains a vulnerability management policy that defines severity classifications, SLAs, and response processes for issues in the platform that may impact customers. We commit to those timelines contractually in our customer agreements, and our security operations are audited, internally and by third parties, to confirm we actually hold to them.
Researchers have a clear channel to responsibly disclose vulnerabilities to us, with explicit safe harbor: we will not pursue legal action against anyone making a good-faith effort to find and report an issue. Researchers working with us are partners, not liabilities, and we treat them that way.
Customers can follow all of it through our Trust Center, which provides self-service access to policies, procedures, security notifications, and third-party assessment reports such as penetration tests. Our Shared Responsibility Model, covered under Evidence of Intrusions below, defines which vulnerability management obligations sit with us and which stay with you. And when we patch a vulnerability that matters to you, you'll see it documented as a regular part of our release notes.
CVEs
The pledge commitment: Demonstrate transparency in vulnerability reporting, including accurate CVE records for the manufacturer's products.
Legion Security's SaaS-only architecture means we don't ship versioned software the way traditional on-prem vendors do, which changes how public vulnerability reporting typically plays out. What doesn't change is our commitment to transparency. We're formalizing a procedure that defines exactly how our security team evaluates and reports vulnerabilities, including issuing CVE records where appropriate, so customers always know what to expect from us. Not just when something goes wrong, but how we'll communicate it.
Evidence of Intrusions
The pledge commitment: Demonstrate a measurable increase in the ability for customers to gather evidence of cybersecurity intrusions affecting the manufacturer's products.
You shouldn't have to pay extra to see what's happening in your own tenant. Every Legion Security customer gets detailed, security-relevant audit logs at no additional cost, exportable to your own data lake for as long as you need to keep them. If something happens, you're never locked out of the evidence.
And here's the part we think matters most for a platform like ours: those logs don't stop at human logins and admin changes. Every action an agent takes during an investigation is recorded, along with the evidence it examined and the reasoning behind what it did. When an AI is doing SOC work, “who did what and why” has to include the AI. With Legion Security, it does.
We also publish a Shared Responsibility Model that spells out which parts of logging, monitoring, and incident response we own and which stay with you, so there's no ambiguity on the day it counts.
Software Supply Chain Security
The pledge commitment: The software manufacturer should maintain and share provenance data of third-party dependencies and have processes to govern its use of, and contributions to, open-source software components.
The pledge stops at seven goals. Your security questionnaires don't, and the topic that comes up most is supply chain. Third-party and open-source components in Legion Security are scanned automatically in source control and CI before anything reaches production, new vendors and dependencies go through a risk review before we adopt them, and a software bill of materials is available to customers through the Trust Center. If it runs inside Legion Security, we can tell you what it is and where it came from.
Looking Ahead
Legion Security's whole premise is that the best security systems don't freeze in place; they keep learning your environment and getting sharper with every investigation. We hold our own security program to the same standard. As the platform grows and the threat landscape shifts, this won't be a static page. We'll keep it current, and we'll keep raising our own bar right alongside it.
Questions about anything above? Dig into the documentation in our Trust Center, or reach out to your Legion Security team directly.
See how Legion Security meets CISA's Secure by Design Pledge with MFA by default, no default passwords, full audit logs, and SOC 2, ISO 27001, and HIPAA-certified security.
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.
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.
SOC continuous improvement fails when insights get buried in closed tickets. Learn a 6-step LLM pipeline that turns investigation notes into action.