Aikido Security

Performs static application security testing across 40+ programming languages using proprietary AST-based analysis engines, then applies AI triage to contextualize findings by exploitability likelihood and reduce noise. The platform ingests code from GitHub/GitLab repositories, parses syntax trees, identifies vulnerability patterns (injection, XSS, SQL injection, etc.), and ranks findings by actual attack surface exposure rather than raw severity scores, filtering out non-exploitable edge cases that traditional SAST tools flag.

Generates and applies automated code patches for detected vulnerabilities across multiple languages and frameworks, directly committing fixes to source repositories via pull requests. The system analyzes vulnerability patterns (injection flaws, weak cryptography, unsafe deserialization, etc.), generates language-specific remediation code using template-based or LLM-assisted generation, and opens pull requests for developer review, enabling hands-off vulnerability remediation without manual code changes.

Detects malware and supply chain attacks in dependencies and containers using 'Aikido Intel' threat intelligence, identifies outdated frameworks and runtimes no longer receiving security updates, and flags suspicious package behavior (typosquatting, dependency confusion, unusual network activity). The system maintains a database of known malicious packages, analyzes package metadata and behavior patterns, and alerts on end-of-life software versions.

Integrates security scanning into CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, etc.) to automatically scan code, dependencies, containers, and infrastructure on every commit/PR, enforce security gates that block deployments failing security thresholds, and provide real-time feedback to developers. The integration triggers scans on push/PR events, evaluates findings against configurable policies, and prevents merges or deployments of code with unacceptable risk levels.

Ranks detected vulnerabilities by actual exploitability likelihood rather than raw CVSS scores, using AI to analyze attack surface, reachability, and environmental context (network exposure, authentication requirements, patch availability, etc.). The system evaluates whether vulnerabilities are actually exploitable in the specific application context, filters out non-reachable code paths, and prioritizes findings by business impact and remediation effort.

Provides centralized dashboard aggregating findings from all scanning modules (SAST, SCA, IaC, containers, cloud, runtime) with customizable views, security metrics (vulnerability trends, remediation rates, coverage metrics), and compliance reporting. The dashboard enables security teams to track security posture over time, identify patterns, and generate reports for stakeholders and auditors.

Enables on-premises or air-gapped deployment of Aikido security scanning via local broker that communicates with cloud control plane, supporting organizations with strict data residency or network isolation requirements. The broker runs security scanners locally, processes findings locally, and syncs only metadata to cloud, enabling enterprise security policies while maintaining centralized management and updates.

Scans project dependencies (npm, pip, Maven, Gradle, Composer, etc.) against vulnerability databases to identify known CVEs in open-source libraries, generates Software Bill of Materials (SBOM) in standard formats, and tracks license compliance issues (dual licensing, restrictive terms). The scanner maintains a real-time index of CVE databases, matches dependency versions against known vulnerabilities, and flags transitive dependencies with security issues, enabling supply chain risk visibility.

Analyzes Terraform, CloudFormation, Kubernetes manifests, and other IaC templates for misconfigurations, compliance violations, and security policy breaches before deployment. The scanner parses IaC syntax trees, evaluates configurations against built-in policy rules (e.g., S3 bucket encryption, IAM overpermissioning, exposed secrets), and identifies drift between intended and actual cloud state, enabling shift-left security for infrastructure provisioning.

Scans Docker images and container registries for vulnerable OS packages and application dependencies, then analyzes runtime behavior to determine which vulnerabilities are actually reachable/exploitable in the running container. The scanner extracts package lists from image layers, matches against CVE databases, and performs static analysis of container entrypoints and running processes to filter out vulnerabilities in unused code paths, reducing false positives from container scanning.

Scans source code, configuration files, and commit history for exposed secrets (API keys, database passwords, private certificates, tokens, etc.) using pattern matching and entropy analysis, then prevents future commits containing secrets via pre-commit hooks and CI/CD integration. The scanner maintains a database of secret patterns (AWS keys, GitHub tokens, Slack webhooks, etc.), analyzes code diffs in real-time, and blocks commits or alerts developers before secrets reach repositories.

Continuously monitors AWS, Azure, and GCP cloud environments for security misconfigurations, compliance violations, and policy breaches by querying cloud APIs for resource state and evaluating against built-in security policies. The scanner maintains real-time inventory of cloud resources (VMs, storage, databases, networking, IAM), identifies deviations from security baselines (unencrypted storage, overpermissioned IAM roles, exposed security groups, etc.), and generates compliance reports for frameworks like CIS, PCI-DSS, and HIPAA.

Automates penetration testing and security assessments using 200+ AI agents that simulate attacker behavior, discover vulnerabilities, and generate audit-grade reports without manual pentester involvement. The system orchestrates agents to perform reconnaissance, exploit detection, privilege escalation, and lateral movement simulation across web applications, APIs, and infrastructure, producing detailed findings and remediation guidance in hours rather than weeks.

Deploys in-application firewall ('Zen') that monitors runtime behavior, detects and blocks injection attacks (SQL injection, command injection, XSS), enforces API rate limiting, and prevents exploitation of known vulnerabilities in production. The protection layer instruments application code or runs as a sidecar, analyzes request/response patterns, identifies malicious payloads in real-time, and blocks attacks before they reach vulnerable code paths.

Analyzes source code for code quality issues, bug risks, and architectural anti-patterns using AI-powered static analysis, identifying problems beyond security (performance bottlenecks, memory leaks, maintainability issues, design flaws). The system evaluates code against best practices, detects common anti-patterns (tight coupling, deep nesting, large functions, etc.), and provides refactoring suggestions with explanations.