figmaJul 29, 2026

How We Secure Figma’s Internal Systems With Agents | Figma Blog (opens in new tab)

Figma built an AI-powered security agent to reduce the manual work involved in investigating SIEM alerts. What began as a retrieval system for finding similar past incidents evolved into an agent that investigates alerts, queries security data, writes fixes, opens pull requests, and retains useful knowledge. The system reportedly reduced alert time-to-resolution by 71% and changed how security engineers handle on-call work. ## The Challenge of Internal Security Operations - Figma’s infrastructure, SaaS tools, identity systems, and employee devices change constantly. - Panther, Figma’s SIEM, monitors these systems and sends alerts to Slack while creating Asana tickets. - On-call engineers previously spent significant time gathering context: - Comparing alerts with incidents from the previous week - Checking whether an existing pull request addressed the issue - Searching Slack discussions for related investigations - Determining whether an alert was new, recurring, or already understood - Existing agent work focused on securing Figma’s codebase, but the team needed a broader system for the many issues detected by its SIEM. ## The RAG Layer: Giving Alerts Historical Context - Figma first created a retrieval-augmented generation system using: - AWS Bedrock Knowledge Bases - Amazon Kendra - Lambda handlers connected to Panther - Each Panther alert is converted into a standardized searchable document. - The system extracts structured information such as: - IP addresses - Usernames and actors - AWS account IDs from ARNs - Alert type, severity, tags, status, and timestamps - Similar alerts are retrieved semantically using the alert title, typically containing the detection name and actor username. - Searches prioritize: - Recent alerts, since investigation procedures evolve - Alerts containing actual investigation context - Comments from engineers rather than merely closed alerts ## Turning Engineer Comments into Institutional Memory - When an on-call engineer comments in a Slack alert thread, Figma captures that text and attaches it to the original alert. - Asana tickets follow a similar process. - The updated alert document is reindexed with: - The accumulated investigation comments - A `has_investigation_context` flag - Future alerts can retrieve these previous explanations and recommendations. - Engineers do not need to adopt a separate annotation workflow; their normal Slack and Asana comments become reusable knowledge. - Each useful investigation effectively makes subsequent similar alerts faster and less expensive to triage. Figma’s approach demonstrates how security agents can build on existing workflows rather than requiring entirely new ones. Starting with searchable historical context allowed the team to progressively develop a broader agentic system while turning everyday investigative work into persistent operational knowledge.

security-automationai-agenttypescriptrag+4

githubJul 9, 2026

How GitHub gave every repository a durable owner (opens in new tab)

Michael Recachinas is a Staff Security Engineer at GitHub who leads large-scale security initiatives. His work focuses on vulnerability management, secure development lifecycle tooling, and developer-first security automation. Throughout his career, he has built scalable systems designed to make secure choices easier for development teams. ## Professional Focus - Leads security programs at GitHub. - Specializes in: - Vulnerability management - Secure development lifecycle tooling - Security automation designed for developers ## Engineering Approach - Builds systems that operate at large scale. - Focuses on integrating security into developers’ workflows. - Aims to make secure practices the easiest and most natural choice. Overall, the passage presents Recachinas as a security engineering leader focused on scalable, practical, and developer-friendly solutions.

security-automationgithubvulnerability-managementsecure-development-lifecycle+1

githubJul 2, 2026

How GitHub used secret scanning to reach inbox zero (opens in new tab)

Michael Recachinas is a Staff Security Engineer at GitHub who leads large-scale security initiatives. His work centers on vulnerability management, secure development lifecycle tooling, and automation that helps developers make secure choices more easily. ### Professional Focus - Leads security programs at scale. - Focuses on: - Vulnerability management - Secure development lifecycle tools - Developer-first security automation ### Experience and Approach - Has built systems designed to operate reliably at large scale. - Emphasizes making secure behavior the easiest option for development teams. The provided text is a professional biography rather than a full technical blog post, so it does not include a specific argument, technical sections, or conclusion to summarize.

security-automationgithubvulnerability-managementapplication-security+1

slackDec 1, 2025

Streamlining Security Investigations with Agents (opens in new tab)

Slack’s Security Engineering team uses a multi-agent AI system to investigate security alerts across billions of daily events. After finding that a single prompt produced inconsistent results, the team replaced it with a controlled workflow of specialized model invocations, structured outputs, and application-level orchestration. The resulting system improves consistency, enables evidence review, and allocates more capable models only where they add the most value. ## From Prompt Prototype to Controlled Workflow - The initial prototype consisted of a roughly 300-word prompt defining: - The analyst’s role - Available data sources - Investigation methodology - Report formatting - Response classifications - A stdio-based MCP server safely exposed selected security data sources to the model. - A coding-agent CLI served as the prototype execution environment. - Results varied significantly: - Sometimes the model cross-referenced evidence effectively. - Other times it reached convenient or unsupported conclusions too quickly. - Prompt refinements helped somewhat, but prompts were ultimately too limited for fine-grained process control. ## Structured Investigation Tasks - Slack decomposed the investigation into a sequence of model invocations. - Each invocation performs one well-defined task and returns a structured output. - The application chains these tasks together and passes only the necessary context between stages. - Structured outputs use JSON schemas to constrain model responses. - They improve predictability, but can still fail when schemas are too complex and remain vulnerable to hallucination or attempts to circumvent constraints. - Guidance such as “question your evidence” became an explicit workflow step rather than merely an instruction in a prompt. ## Persona-Based Agent Architecture - Slack drew inspiration from research on meta-prompting, multi-persona collaboration, and security tabletop exercises. - Instead of representing multiple personas within one model call, Slack implemented each persona as an independent model invocation. - Every agent/task pair has: - A defined responsibility - A carefully designed output structure - Specific prompts and instructions - Potentially different models and tools - The application orchestrates the agents and controls how knowledge moves through the investigation. ## The Investigation Loop - **Director agent** - Guides the investigation from beginning to end. - Forms questions for domain experts. - Uses a journaling tool to plan and organize progress. - Decides how to continue based on reviewed findings and timelines. - **Expert agents** - Investigate questions using specialized knowledge and data sources. - Slack currently uses four domains: - **Access:** Authentication, authorization, and perimeter services - **Cloud:** Infrastructure, compute, orchestration, and networking - **Code:** Source code and configuration management - **Threat:** Threat intelligence and analysis - **Critic agent** - Acts as a meta-expert reviewing domain findings. - Applies a defined rubric to assess quality. - Adds analysis and credibility scores to individual findings. - Helps identify reliable evidence and reduce hallucinations. - Returns its conclusions to the Director, closing the investigation loop. - The Critic’s mildly adversarial role provides an independent challenge to expert conclusions. ## The Knowledge Pyramid - Different stages use different model capabilities and costs. - Domain experts operate at the base: - They query complex data sources. - They may make many tool calls. - Processing their results can consume substantial tokens. - The Critic reviews the larger set of expert findings and identifies the most valuable or credible evidence. - Higher-level reasoning can therefore use a smaller, more focused context. - Because each stage is independent, Slack can select different model versions, prompts, tools, and output formats for different tasks. Slack’s main recommendation is to treat complex agent behavior as an explicitly orchestrated workflow rather than relying on one increasingly elaborate prompt. Specialized agents, structured outputs, independent critique, and tiered model usage provide more predictable investigations while preserving the flexibility of AI-assisted analysis.

security-automationllmai-agentmodel-context-protocol+3

kakaoNov 30, 2025

YEYE is Watching – (opens in new tab)

Kakao developed YEYE, a dedicated Attack Surface Management (ASM) system, to proactively identify and manage the organization's vast digital footprint, including IPs, domains, and open ports. By integrating automated scanning with a human-led Daily Security Review (DSR) process, the platform transforms raw asset data into actionable security intelligence. This holistic approach ensures that potential entry points are identified and secured before they can be exploited by external threats. ## The YEYE Asset Management Framework * Defines attack surfaces broadly to include every external-facing digital asset, such as subdomains, API endpoints, and mobile APKs. * Categorizes assets using a standardized taxonomy based on scope (In/Out/Undefined), type (Domain/IP/Service), and identification status (Known/Unknown/3rd Party). * Implements a labeling system that converts diverse data formats from multiple sources into a simplified, unified structure for better visibility. * Establishes multi-dimensional relationships between assets, CVEs, certificates, and departments, allowing teams to instantly identify which business unit is responsible for a newly discovered vulnerability. ## Daily Security Review (DSR) * Operates on the principle that "security is a process, not a product," bridging the gap between automated detection and manual remediation. * Utilizes a rotating group system where security engineers review external feeds, public vulnerability news, and YEYE alerts every morning. * Focuses on detecting "shadow IT" or assets deployed without formal security reviews to ensure all external touchpoints are accounted for. ## Scalable and Efficient Scanning Architecture * Resolved internal network bandwidth bottlenecks by adopting a hybrid infrastructure that leverages public cloud resources for high-concurrency scanning tasks. * Developed a custom distributed scanning structure using schedulers and queues to manage multiple independent workers, overcoming the limitations of single-process open-source scanners. * Optimized infrastructure costs by identifying the "sweet spot" in server specifications, favoring the horizontal expansion of medium-spec servers over expensive, high-performance hardware. * Mitigates service impact and false alarms by using fixed IPs and custom User-Agent (UA) strings, allowing service owners to distinguish YEYE’s security probes from actual malicious traffic. To effectively manage a growing attack surface, organizations should combine automated asset discovery with a structured manual review process. Prioritizing data standardization and relationship mapping between assets and vulnerabilities is essential for rapid incident response and long-term infrastructure hardening.

security-automationcloud-securitynetwork-securitycve+3