Flowharmonic Agentic AI workflows for SOC Teams
FlowHarmonic.io uses a mixture of open-source models, security knowledge base, NIST CSF, NVD, MITRE Attack data combined with reinforcement learning to deliver exceptional accuracy for your Security Operations Agentic AI Workflows.
- Multi-Step Reasoning
- Long Security Contextual Understanding
- Function-Calling
- Multi-agent collaboration framework
- Automated Workflows
What problem are we solving?
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗹𝗼𝘄-𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗳𝗿𝘂𝗶𝘁 𝗶𝘀𝗻’𝘁 𝗺𝗼𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲. 𝗦𝘂𝗿𝗲, 𝟴𝟬-𝟵𝟬% 𝗼𝗳 𝗺𝘆 𝗮𝗹𝗲𝗿𝘁𝘀 𝗮𝗿𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱, 𝗯𝘂𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗦𝗢𝗖 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝘁𝗶𝗺𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗿𝗲𝗺𝗮𝗶𝗻𝗶𝗻𝗴 𝟭𝟬%, 𝘄𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗽𝗼𝗶𝗻𝘁?
Everyday SOC Analyst is looking for ad-hoc answers quickly. AI Agents can be used to make sure that it was easy for SOC Analyst teams to get what they needed in order to keep the organization secure and the most trustworthy platform.
- Are we affected by vulnerability X?
- Is vulnerability X on devices exposed to untrusted networks?
- Who is responsible for patching host A?
- How do I patch vulnerability X on Windows 11?
- What is the highest risk vulnerability on my devices?
- What services are running on host X and who are the owners?
Why FlowHarmonic Agents?
FlowHarmonic Agents are uniquely designed to make Agentic workflows better
Let’s be honest, 60% - 65% accuracy is not something other agents would want in production.
Current State of the art LLM Agents fall short on accuracy for moderate to complex reasoning tasks, especially if function/tool-calling is involved. This makes SOC Agents even harder due to the fact that cyber security is massively unstructured.
For simple operations, models like GPT4 and Claude3.5-Sonnet can get more than 90% accuracy, but that quickly falls to a mere 60% - 65% range on multi-step reasoning tasks, rendering them highly unreliable.
FlowHarmonic Agents Outperform any security Agents that are built using open source models.
- Combine as many agents as necessary to build StoryBooks
- Automate the StoryBooks
- Enables Cost Effective Integration of open source models
- Control the tokens that can be used for Agents
On complex agentic reasoning tasks, combining multi-step workflows with function-calling, code generation and structured outputs.
