AI · alternatives · 2026
24 alternatives to GitHub
Modular AI threat detection & GDPR logging for LLMs
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
ShieldForce▲76ShieldForce safeguards your business from cyber threats
2024 · shieldforce.io · its alternatives →
- 2

The AI-powered AppSec platform that identifies vulnerabilities with comprehensive penetration tests and provides actionable remediation insights.
2024 · beaglesecurity.com · its alternatives →
- 3

The modern standard in AML compliance through AI agents
2023 · flagright.com · its alternatives →
- 4
SMS Shield▲112Machine Learning-based SMS spam filter for iOS 11
2017 · apps.apple.com · its alternatives →
- 5
Shieldstral▲104Define safety at runtime for text and images
Aug 2026 · mistral.ai · its alternatives →
- 6
PrivacyPal▲110The Browser Extension for AI Governance & Security.
Jan 2026 · privacypal.ai · its alternatives →
- 7
AI Hive ▲53Build, deploy, and run all your AI agents in one platform.
May 2026 · app.aihive.global · its alternatives →
- 8TL
2023 · tinyllms.vercel.app · its alternatives →
- 9
CodeShield▲78Detect and fix IAM privilege escalations in your AWS account
2022 · codeshield.io · its alternatives →
- 10

- 11

Automated security assistant for developers
2020 · shieldfy.io · its alternatives →
- 12

- 13

A zero-trust security layer between your apps and LLMs
Jan 2026 · its alternatives →
- 14

Hey HN, Hakim here from Fini (YC S22), a startup focused on providing automated customer support bots for enterprises that have a high volume of support requests. Today, one of the largest use cases of LLMs is for the purpose of automating support. As the space has evolved over the past year, there has subsequently been a need for evaluations of LLM outputs - and a sea of LLM Evals packages have been released. "LLM evals" refer to the evaluation of large language models, assessing how well these AI systems understand and generate human-like text. These packages have recently relied on…
2024 · github.com · its alternatives →
- 15

We're excited to launch compliant-llm: an open-source toolkit that helps infosec and compliance teams audit AI agents against regulatory frameworks like NIST AI RMF, ISO 42001, and OWASP Top 10. Infosec and compliance teams are now responsible for tracking security and compliance risks of a growing number of AI agents across external and internal apps and third-party vendors. compliant-llm gives you a way to: - Define and run comprehensive red-teaming tests for AI agents - Maps test outcomes to compliance frameworks like NIST AI RMF - Generate detailed audit logs and documentation -…
2025 · github.com · its alternatives →
- 16

Empowering you and your loved ones to prevent digital fraud
2024 · seniorshield.ai · its alternatives →
- 17

Self-hosted PII firewall for LLMs — policies, audit trail
Jun 2026 · github.com · its alternatives →
- 18

Sanitize your AI prompts before you leak secrets.
Dec 2025 · my-privacy-shield.netlify.app · its alternatives →
- 19
Shieldly▲2AI-powered AWS security — find risky IAM before it ships
Jun 2026 · shieldly.io · its alternatives →
- 20

AI Act & GDPR evidence for SaaS LLM teams.
May 2026 · aiactsaas.com · its alternatives →
- 21
Trust & Security Infrastructure for Enterprise AI
Jun 2026 · aetherguard.ai · its alternatives →
- 22

Runtime governance for AI agents. Allow, warn, or block every model and tool call before it commits. Hash-chained audit for every decision. Compliance packs for SOC 2, HIPAA, PCI DSS, EU AI Act, SR 11-7, and FDA CSA. Apache 2.0. - sseshachala/conductai
Aug 2026 · github.com · its alternatives →
- 23

New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.
2023 · github.com · its alternatives →
- 24
Custosa▲15Permission and compliance layer for Enterprise AI
Mar 2026 · custosa.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →