Directory CybersecurityData Security & DLPACME.sh

ACME.sh

Open Source

About ACME.sh

ACME.sh is a lightweight, Unix shell script-based ACME protocol client designed for automating the issuance, renewal, and installation of SSL/TLS certificates. It supports a wide range of certificate authorities including Let's Encrypt and ZeroSSL, and offers compatibility with multiple Unix-like operating systems such as Linux distributions, FreeBSD, macOS, and others. The client supports advanced certificate types including ECDSA, SAN, and wildcard certificates without requiring root or sudo access, making it suitable for secure, automated certificate management in diverse enterprise environments.

Targeted at enterprises seeking a simple yet powerful solution for certificate lifecycle management, ACME.sh eliminates dependencies on external languages like Python and integrates easily into existing Unix shell environments. Its Docker readiness and support for cron job notifications enhance operational efficiency and reliability. The product is particularly valuable for organizations prioritizing open-source solutions for cybersecurity and data security, enabling seamless integration with DevOps workflows and infrastructure automation.

How to evaluate Data Security & DLP

CIOPages Research Team evaluation frameworks for this category — not an assessment of ACME.sh. This category covers two kinds of product, so both frameworks are shown; buyers usually need one of them.

Data Loss Prevention (DLP)

30%

Classification Accuracy & Detection

Exact data match (EDM) and indexed/document matching for structured records, ML and trainable classifiers for unstructured content, fingerprinting, OCR on images and screenshots, and the false-positive rate observed on YOUR data — not the vendor’s sample set

25%

Channel Coverage & Enforcement

Consistent policy across endpoint (USB, clipboard, print, local sync), network/web, email, sanctioned and unsanctioned cloud/SaaS, and increasingly GenAI prompts — with real enforcement (block, encrypt, quarantine, justify) on each channel, not just monitoring

15%

Insider Risk & Behavioral Context

Risk-adaptive enforcement that tightens around high-risk users, UEBA and activity timelines, integration with IRM, and the ability to back-test a new policy against historical activity before it goes live

15%

Data Discovery & DSPM Convergence

At-rest discovery across endpoints, file shares, databases, and cloud stores; data-security-posture visibility (where sensitive data lives, who can access it); and whether discovery, DLP, and DSPM share one classification model and console

10%

Tuning, Operations & Incident Workflow

Quality of the incident queue and remediation workflow, policy simulation/monitor mode, end-user coaching and self-remediation, SIEM/SOAR/XDR integration, and the realistic FTE load to run the program day to day

Deployment Model & Architecture

Cloud-delivered vs. on-prem servers and appliances, endpoint agent footprint and performance impact, data residency for incident evidence, and fit with an existing SSE/SASE or M365/Workspace estate

Data Security Posture Management (DSPM)

25%

Classification Accuracy & Sensitive-Data Discovery

Precision and recall on your data — structured and unstructured — beyond regex and dictionaries; AI/ML and LLM-based context detection for proprietary IP, source code, and free-text documents; secrets and credential discovery; false-positive rate; ability to learn custom data types without heavy rule-writing

20%

Data-Store & Environment Coverage

Depth across managed cloud databases (RDS, Cosmos, Cloud SQL), object storage, data warehouses (Snowflake, BigQuery, Redshift, Databricks), SaaS apps (M365, Salesforce, Google Workspace), on-prem file shares and NAS, and shadow/unmanaged stores; agentless connect-and-scan vs. agent or proxy requirements; does data leave your tenant?

20%

Risk Context: Identity, Access & Exposure

Linking each sensitive store to who can access it (data access governance), effective permissions across human and machine identities, public/internet exposure, third-party and external sharing, and — ideally — reachable attack paths; the quality of the “exposed regulated data” shortlist versus a flat inventory

15%

AI & LLM Data Security (AI-SPM)

Discovery of sensitive data in AI training sets, RAG sources, and vector stores; governance of data flowing into and out of copilots and LLM apps; detection of shadow AI; oversharing assessments for tools like Microsoft 365 Copilot; data-use policy enforcement at the AI boundary

10%

Remediation, DDR & Workflow Integration

Guided and automated remediation (revoke access, fix permissions, quarantine, tokenize/mask), real-time data detection & response on data movement and exfiltration, ownership routing to the right team, and clean integration with SIEM/SOAR, ITSM, and existing DLP rather than a siloed dashboard

10%

Compliance, Privacy & Reporting

Out-of-the-box mapping to GDPR, CCPA/CPRA, HIPAA, PCI DSS and emerging AI regulation; data-residency and sovereignty detection; support for privacy workflows (DSAR/RoPA) where the same engine serves the privacy office; audit-ready evidence, data-risk trend reporting, and RBAC/SSO on the console

Other Data Security & DLP Vendors

Related Buyer Guides

Independent evaluation frameworks for this category.

API Security Platforms

The API is now the primary attack surface, and a WAF pointed at it isn't API security. Evaluate Salt, Akamai (Noname), Imperva, F5, Traceable, Wallarm, Cequence, and Data Theorem on whether they actually discover your shadow APIs and catch BOLA and business-logic abuse at runtime — not just sign endpoints off as “protected.”

Attack Surface Management (ASM / CTEM)

Evaluate Wiz, CrowdStrike Falcon Surface, Microsoft Defender EASM, Cortex Xpanse, Censys, CyCognito, Tenable, and runZero across EASM, CAASM, and platform-embedded camps — judged on discovery accuracy and how cleanly the surface feeds prioritization, not how many assets it claims to find.

Cloud Access Security Broker (CASB)

Evaluate Netskope, Microsoft Defender for Cloud Apps, Zscaler, and Palo Alto for SaaS security, shadow IT discovery, and data protection.

This profile was compiled by CIOPages from public sources with AI assistance, and may be incomplete or out of date. It is informational only and not an endorsement. Represent this vendor? Claim this listing or .