The MIT AI Risk Repository: A Detailed Guide to the World’s Largest AI Risk Database

 MIT AI Risk Repository 

The MIT AI Risk Repository is a major research initiative created to provide the world’s most comprehensive, structured, and unified resource on risks posed by artificial intelligence. It functions as a living, continuously updated database of AI risks, taxonomies, and documented sources, developed by the MIT AI Risk Initiative / MIT FutureTech Group.

It is publicly accessible at airisk.mit.edu.

1. What the MIT AI Risk Repository Is

According to MIT, the AI Risk Repository is:

• A centralized, living database of AI-related risks, currently listing 700–1700+ risks depending on the version referenced (MIT's web version lists 1700+, while the academic paper documents 777 risks).
• Compiled from dozens of academic, government, and industry AI frameworks (43–74 frameworks, depending on the version).
• Designed to create a shared vocabulary for researchers, policymakers, auditors, and companies when discussing AI risks.
• Open-access and designed to be extensible, meaning new risks can be added as the field evolves. 

The repository aims to unify a fragmented AI governance landscape and support future policy, regulation, audits, and safe AI development practices.

2. Core Components of the Repository

MIT describes the repository as having three primary components:

A. The AI Risk Database

Contains:

• 700–1700+ documented AI risks
• Direct links to source material (papers, frameworks, reports)
• Quotes and page numbers verifying each risk 

This database enables:

• Filtering risks by type, cause, domain, or scenario
• Downloading risks in formats like Google Sheets or OneDrive
• Reviewing evidence and citations for each risk

B. The Causal Taxonomy of AI Risks

This taxonomy classifies how a risk arises based on three dimensions:

1. Entity

• Human
• AI
• Other/ambiguous

2. Intentionality

• Intentional
• Unintentional
• Undefined

3. Timing

• Pre-deployment
• Post-deployment
• Unspecified

This answers:

Who caused the risk?

Was it intentional?

When does it arise?

C. The Domain Taxonomy of AI Risks

This organizes risks into 7 major domains and 23–24 subdomains.

The seven high-level domains are:

1. Discrimination & toxicity

2. Privacy & security

3. Misinformation

4. Malicious actors & misuse

5. Human-computer interaction issues

6. Socioeconomic & environmental impacts

7. AI system safety, failures & limitations

MIT notes, for example, that privacy and security risks appear in 70%+ of the reviewed frameworks, while risks such as AI rights and welfare appear in <1%.

3. How the Repository Was Created

The repository was built via a systematic meta-review of existing AI risk frameworks.

Researchers: Peter Slattery, Neil Thompson, and a multi-disciplinary MIT team. [ide.mit.edu], [arxiv.org]

The process involved:

1. Reviewing 43–74 AI governance documents

2. Extracting every explicit AI risk described

3. An expert consultation process

4. Creating high-level and mid-level taxonomies

5. Publishing the database and taxonomies openly

The academic paper describing this process is titled:

“The AI Risk Repository: A Comprehensive Meta‑Review, Database, and Taxonomy of Risks From Artificial Intelligence” (2024–2025). 

4. Why the MIT AI Risk Repository Matters

A. Establishes a Shared Language

The AI governance ecosystem is fragmented. Different industries, researchers, and governments use inconsistent terminology. The MIT repository unifies them under one standard. [mitsloan.mit.edu]

B. Improves AI Safety and Compliance

Organizations can use the repository to:

• Identify relevant risks
• Prioritize risk mitigation
• Build audits and assessments
• Improve AI governance frameworks

C. Helps Policymakers

Regulators can more clearly understand:

• Where risks occur
• How common they are
• How they compare across industries 

D. Tracks Underexplored Risk Categories

For example, MIT found:

• Privacy & security risks appear in >70% of risk frameworks
• Misinformation risks appear in only ~40%
• AI welfare/rights appear in <1% 

This highlights research gaps.

E. Supports Research, Education, and Standardization

The repository is used for:

• Academic research
• Policy development
• Corporate risk audits
• Curriculum design

5. Examples of Risks Found in the Repository

The repository documents risks across many categories, including:

• Bias and discrimination in model outputs
• Privacy breaches/data leakage
• Deepfake misinformation
• AI-enabled cyberattacks
• Model hallucinations
• Autonomous system failures
• Socioeconomic displacement
• Environmental resource consumption 

Each risk is paired with:

• Citations
• Exact quotes
• Evidence
• Categorization by taxonomy

6. How to Use the MIT AI Risk Repository

MIT suggests several uses:

• Search for risks relevant to a specific AI system
• Explore causal and domain factors to build risk models
• Build governance frameworks and compliance plans
• Teach AI risk management in educational settings
• Monitor emerging risks as the database updates

7. Strengths and Limitations (Based on Research Commentary)

Strengths 

• Open-access, transparent, regularly updated
• Most comprehensive resource of its kind
• Useful taxonomies (causal and domain-based)
• Unified framework that integrates 700+ risks
• Valuable for practical AI governance

Limitations

• Some risks may be high-level or ambiguous
• Interpretation depends on user expertise
• Coverage of novel or speculative risks is still evolving
• Some domains are underrepresented (e.g., AI rights)

8. Summary

The MIT AI Risk Repository is one of the most important AI governance tools available today. It combines:

• A living database of 700–1700+ AI risks
• A causal taxonomy explaining how risks arise
• A domain taxonomy categorizing risk areas
• Full citations and evidence
• Open-access resources for researchers, businesses, auditors, and policymakers

Its purpose is to standardize AI risk vocabulary, support governance, and improve global understanding of AI risks in a rapidly evolving field.

OWASP GenAI Security Project: The Comprehensive Framework for Securing LLMs and Agentic AI

 OWASP GenAI Security Project

What it is & why it exists

• A flagship, open-source initiative by OWASP focused on identifying, mitigating, and documenting security and safety risks in generative AI (LLMs and agentic systems).
• Evolved from the original “Top 10 for LLM Application Security” (launched May 2023) into a broader project with 600+ experts, 130+ companies, and ~8,000 community members. 

Core deliverables & guidance

OWASP Top 10 for LLMs (2025)

• Lists the most critical vulnerabilities in LLM-based apps (e.g., prompt injection, RAG issues, DoS). 
• Widely used by regulators and standards bodies (NIST, MITRE).
• Updated regularly, v3 released at the end of 2024, added RAG-specific risks.

Agentic AI (autonomous agents)

• Introduced Top 10 for Agentic Applications, covering threats from AI that act (not just output text). 
• Includes guides like:
• Threats & Mitigations taxonomy
• Multi-Agent Threat Modeling
• Securing Agentic Applications
• Agentic Security Solutions Landscape (DevOps–SecOps lifecycle).

Governance, compliance & tooling

• Expanded beyond vulnerabilities to include:
• Governance checklists (e.g., for CISOs)
• Deepfake response guides
• Center of Excellence setup
• AI Security Solutions Landscape. 
• COMPASS framework (Sept 2025): a threat-defense dashboard with scoring (impact/likelihood), runbook, spreadsheet tool, designed for ongoing risk assessment.

Why it matters in practice

• DevOps relevance: AI agents often get access to code repos, CI/CD, and cloud APIs, so a prompt injection or misconfigured agent can cause real damage.
• Focuses on agentic behavior, multi-step planning, tool use, memory, and inter-agent coordination, introducing new failure modes. 
• Community-driven, globally translated (Spanish, German, Chinese, Portuguese, Russian), and aligned with standards like ISO/IEC and the EU AI Act.

Quick comparison: LLM vs Agentic focus

Bottom line: OWASP GenAI Security is now the go-to open, community-backed framework for securing generative AI, from basic LLM apps to fully autonomous agents. It offers practical tools, threat taxonomies, and governance guidance that align with real-world DevOps and compliance needs.

Understanding Spine‑and‑Leaf Topology: The Modern Standard for Data Center Networks

 Spine‑and‑Leaf Topology

Spine‑and‑leaf is a two‑tier network architecture designed to deliver:

• predictable low latency
• high bandwidth
• full‑mesh connectivity
• scalable east–west traffic handling

It is widely used in modern data centers, especially those running virtualization, containers, microservices, and cloud workloads.

Architecture Overview

The architecture has only two layers:

1. Leaf Layer (Access Layer)

• These switches connect directly to servers, storage, and edge devices.
• Every leaf switch connects to every spine switch.
• Leaf switches do not connect to other leaf switches.

Leaf Responsibilities:

• Provide the access point for servers
• Handle local switching
• Load balance traffic across multiple spines
• Participate in routing (typically with ECMP: Equal-cost multi-path)

2. Spine Layer (Core Layer)

• The spine is the backbone of the network.
• Spine switches connect only to leaf switches, not to each other.
• Their main purpose is to ensure high‑speed, non‑blocking packet forwarding.

Spine Responsibilities:

• Provide high‑capacity fabric
• Maintain minimal and predictable latency
• Perform simple routing functions (usually L3 underlay)

How Spine-and-Leaf Works

1. Every leaf connects to every spine

• This creates a full-mesh connection pattern, enabling multiple equal-cost paths.

2. Traffic uses ECMP (Equal Cost Multi-Pathing)

• Since all paths are of the same cost, traffic can be load‑balanced across all spines.

3. Predictable latency

• The path between any two servers is always:
• Server → Leaf → Spine → Leaf → Server
• This constant hop count gives predictable performance.

Why Spine‑and‑Leaf Is Used

1. Massive Scalability

To scale, you simply:

• Add more leaf switches to increase server ports
• Add more spine switches to increase total bandwidth

No redesign required.

2. Great for East‑West Traffic

• Modern data center applications generate mostly east‑west traffic (server-to-server), not server-to-internet.
• Spine‑and‑leaf is built exactly for that.

3. High Throughput and Low Latency

• All links are active and load-balanced.

4. Simple, modular design

• Easy to expand without downtime.

5. Supports VXLAN/EVPN

• Very common for multi-tenant cloud environments.

Topology Diagram (Simple)

           Spine Layer

        +---------+   +---------+

        | Spine 1 |   | Spine 2 |

        +----+----+   +----+----+

             \           /

              \         /

               \       /

                \     /

       +---------+   +---------+

Leaf Layer       |   |

       | Leaf 1  |   | Leaf 2  |

       +----+----+   +----+----+

            |            |

      +-----+----+  +----+------+

      | Server A |  | Server B |

      +----------+  +-----------+

Key Design Characteristics

1. Non-blocking architecture

• The total uplink capacity from each leaf equals or exceeds the downlink capacity to servers.

2. Multistage Clos network

• Spine‑and‑leaf is a specific case of a Clos topology, designed to minimize congestion.

3. Supports extremely large fabrics

• Hyperscale companies (AWS, Azure, Google) use expanded multi‑tier spine‑and‑leaf designs.

How It Compares to Three‑Tier Architecture

When to Use Spine-and-Leaf

Use it when:

• You run a data center (small or large)
• You need high bandwidth between servers
• You use virtual machines, Kubernetes, and microservices
• You require VXLAN/EVPN overlays
• You want linear scalability

Not necessary for:

• Small office networks
• Simple LANs

Summary

Spine-and-leaf topology is a modern, scalable, and high‑performance network design that provides predictable latency and full‑mesh connectivity by connecting every leaf switch to every spine switch.

It supports multi‑pathing, heavy east‑west traffic, and cloud-native architectures, making it the de facto standard architecture for modern data centers.

Fibre Channel: A Complete Guide to High‑Speed, Enterprise‑Grade Storage Networking

 Fibre Channel

Fibre Channel (FC) is a high‑speed data transfer technology designed primarily for Storage Area Networks (SANs). It connects servers, storage arrays, and data centers using a dedicated, low‑latency, lossless fabric. FC is known for:

• High performance
• Low latency
• Reliability
• In‑order, lossless delivery of block data

It is widely used in enterprise environments for mission‑critical workloads, including databases, virtualization, OLTP, and banking systems.

How Fibre Channel Works

Fibre Channel transports data using specialized infrastructure:

1. Physical Media

• Primarily optical fiber (multi‑mode or single‑mode)
• Can also run on copper

Distance capabilities:

• Up to 500m on multi‑mode
• Up to 10km on single‑mode

 2. Speeds

Modern FC generations support:

• 8G, 16G, 32G, 64G, and up to 128G per second

3. Protocol

FC uses Fibre Channel Protocol (FCP) to transport SCSI commands over the fabric.

This ensures:

• Error correction
• Flow control
• Reliable, consistent delivery

Fibre Channel Topologies

Fibre Channel supports several network arrangements:

1. Point-to-Point

Direct link between two devices.

• Simple and fast
• Full bandwidth per connection

2. Fibre Channel Arbitrated Loop (FC‑AL)

Devices form a one‑way ring:

• Up to 126 devices
• Uses arbitration to determine who can transmit
• Mostly obsolete externally, but still used internally in switches

3. Switched Fabric

Most common modern FC topology:

• Uses Fibre Channel switches to form a fabric
• Scalable to thousands of devices
• Enables zoning, redundancy, and load balancing

Fibre Channel Port Types

Various ports enable flexibility:

Key Advantages of Fibre Channel

1. High Performance & Low Latency

• FC delivers extremely fast, predictable transfer speeds with minimal latency, ideal for real‑time workloads.

2. Reliability & Fault Tolerance

Built‑in mechanisms include:

• Error correction
• Data integrity checks
• Redundant pathing

3. Security

• FC fabrics are naturally isolated from IP traffic.
• Zoning allows precise access control.

4. Scalability

Supports:

• Hundreds to thousands of connected devices
• Enterprise‑wide SAN deployments

5. Lossless Transport

• Unlike Ethernet (unless enhanced with DCB), FC is designed to be lossless, ensuring data is always delivered.

Common Use Cases

Fibre Channel is widely deployed for:

• Enterprise SANs
• Database hosting (OLTP)
• Virtualized environments
• Disaster recovery replication
• High‑performance computing

It remains the backbone of 90% of global SAN installations, according to the Fibre Channel Industry Association.

Standards and Industry Evolution

• Development began: 1988
• First ANSI standard: 1994 (FC‑PH)
• Current standards: FC‑PI (physical interface) and FC‑FS (framing & signaling)

Latest generations include 128GFC, enhancing reliability and performance for next‑generation data workloads.

Summary

Fibre Channel is:

• A dedicated, lossless, high‑speed storage networking technology
• Ideal for SANs requiring high bandwidth, low latency, and strong reliability
• Built on a robust fabric of switches, optical media, and SCSI‑based protocols
• Still evolving, with 128GFC supporting modern hybrid cloud and virtualization demands

Its mature ecosystem and unmatched reliability ensure that Fibre Channel remains a critical backbone of enterprise data centers, even in the age of cloud and NVMe‑over‑Fabrics.

LDAP Injection Attacks: How They Work and How to Prevent Them

LDAP Injection Attack

LDAP Injection is a type of injection attack where an attacker manipulates LDAP (Lightweight Directory Access Protocol) queries by injecting malicious input into fields that are used to build LDAP filters.

It is similar in concept to SQL injection, but targets LDAP directory services such as:

• Active Directory
• OpenLDAP
• Oracle Internet Directory
• Novell eDirectory

LDAP is often used for:

• Authentication (“log in with your corporate account”)
• Authorization (retrieving user permissions)
• Directory lookups (searching for users, groups, devices)

When developers build LDAP queries using unsanitized user input, attackers can alter query logic and access unauthorized data, or bypass authentication entirely.

How LDAP Queries Work

A typical LDAP search filter looks like this:

(&(objectClass=person)(uid=jsmith))

This means:

• Find entries that are person objects
• With a uid of jsmith

When a login form accepts a username and password, the backend might form a query like:

(&(uid={username})(password={password}))

If user input is inserted directly, it becomes vulnerable.

How LDAP Injection Happens

Suppose a login form uses this filter:

(&(uid={USER})(userPassword={PASS}))

If an attacker enters:

• Username: *
• Password: *)(&(uid=*))

The resulting LDAP filter becomes:

(& (uid=*) (userPassword=*) )(&(uid=*) ))

This can cause:

• Always‑true conditions
• Bypassed authentication
• Disclosure of all directory entries

Common LDAP Injection Attack Techniques

1. Authentication Bypass

Attackers input special LDAP wildcard characters like:

*) (|

Example malicious input:

Username:

admin*)(|(uid=*))

Resulting filter:

(&(uid=admin*)(|(uid=*))(password=…))

This filter will return all users, potentially allowing authentication without knowing the password.

2. Data Extraction

Attackers alter search filters to reveal:

• Usernames
• Email addresses
• Group memberships
• Other directory attributes

Example injection:

*)(mail=*)

This changes the query to return every entry with an email address.

3. Privilege Escalation

If an LDAP-based app determines permissions by querying group membership, an attacker may alter the group filter to trick the application into thinking they belong to an admin group.

4. Denial of Service (DoS)

Injecting heavy filters like nested OR conditions can overload the directory server:

*)(|(uid=*)(cn=*))(foo=*

Why LDAP Injection Is Dangerous

LDAP injection attacks can allow attackers to:

• Bypass authentication
• Retrieve sensitive records (users, groups, credentials, metadata)
• Escalate privileges
• Modify directory entries (if the app allows write access)
• Compromise entire identity infrastructure (e.g., Active Directory)

Since directory services control authentication/authorization, LDAP injection is often more damaging than SQL injection.

How to Prevent LDAP Injection

1. Use Parameterized LDAP Queries

• Instead of concatenating strings, use safe parameterized APIs (varies by language).

2. Validate and Sanitize User Input

• Reject special LDAP filter characters:
• (, ), *, |, &, =
• Allow only expected characters in usernames, emails, etc.

3. Escape LDAP Special Characters

• Properly escape user input before using it in queries.

4. Enforce Least Privilege on LDAP Accounts

• Ensure the application binds to a user with read-only access and a limited scope.

5. Implement Strong Authentication Controls

• Multi-factor authentication reduces the impact of bypass attempts.

6. Use Application Firewalls

• WAFs/IDSes can detect injection patterns.

Example Secure LDAP Query (Escaped Input)

If a user inputs:

jsmith

The backend safely escapes it:

jsmith becomes jsmith   (no change)

But if the user enters:

*)(|(uid=*))

It is escaped to:

\2a\29\28\7c\28uid=\2a\29\29

This prevents query manipulation.

Summary

LDAP Injection occurs when:

• User input is directly inserted into LDAP queries.
• Attackers exploit special characters and LDAP syntax.
• This leads to authentication bypass, data theft, privilege escalation, or server disruption.

LDAP injection is prevented by:

• Parameterized queries
• Input validation + escaping
• Least privilege directory access
• Strong authentication controls

CREST: The Gold Standard for Professional Penetration Testing

 What is CREST in Penetration Testing?

CREST (Council of Registered Ethical Security Testers) is an international, not‑for‑profit accreditation and certification body for the cybersecurity industry. It sets professional standards for penetration testers and security service providers. Its certifications and company accreditations provide assurance that pentesting is performed ethically, competently, and using consistent, validated methodologies.

CREST plays two main roles:

1. Certifying individuals — penetration testers and threat‑intelligence/incident‑response specialists.

2. Accrediting organizations — pentesting consultancies that meet CREST’s operational, technical, and quality standards.

Why CREST Exists

CREST was created to address the risks of unregulated and inconsistent penetration testing, ensuring companies can trust the people and organizations performing these services. Its mission includes:

• Providing a “stamp of approval” for high‑quality pentesting.
• Ensuring pentesters follow strict ethical, legal, and methodological standards.
• Validating the technical competence of testers via rigorous hands‑on exams.
• Ensuring member companies meet quality‑assurance and data‑handling standards.

With hundreds of accredited organizations worldwide and thousands of certified testers, CREST has become one of the most recognized standards in professional pentesting.

What CREST Guarantees in a Pentest

Working with CREST‑certified testers or CREST‑accredited companies comes with strong assurances:

Repeatable, audit‑grade methodologies

• CREST mandates documented, defensible processes for scoping, testing, evidence gathering, and reporting.

Technically vetted testers

• Individuals must pass examinations that simulate real pentesting scenarios and require demonstrable skill.

Ethical & legal compliance

• A strict code of conduct ensures clear boundaries, particularly in sensitive or regulated environments.

Meaningful, technically sound reports

• CREST emphasizes producing actionable evidence (logs, PoC traces, reproducible exploit paths).

Industry and regulatory recognition

• CREST certifications are globally recognized and often required or preferred by buyers of security services.

CREST in the Pentesting Workflow

CREST outlines structured pentesting processes to ensure consistency across engagements. This includes:

• Scoping under defined rules of engagement
• Pre‑engagement preparation
• Methodical vulnerability discovery
• Exploitation and evidence gathering
• Risk analysis and prioritization
• Remediation guidance

It also supports multiple pentesting domains:

• Web application
• Network
• Mobile
• Cloud
• API
• Vulnerability Assessment
• Intelligence‑led (STAR) testing

CREST Certification Path for Pentesters

CREST provides a full career pathway from entry‑level to highly advanced testing roles.

1. CPSA — CREST Practitioner Security Analyst

• Entry‑level exam covering fundamental pentesting knowledge.

2. CRT — CREST Registered Penetration Tester

• Intermediate, hands‑on exam assessing ability to test infrastructure and web apps under time‑boxed conditions.
• Delivered via Pearson VUE on a locked‑down Kali Linux environment. 

3. CCT (INF / APP) — CREST Certified Tester

Advanced specialization:

• Infrastructure (CCT INF)
• Application (CCT APP)

4. CCRTS / CCRT M — CREST Red Team certifications

• For advanced offensive operators and managers.
• Many governments (e.g., the UK) align CREST exams with public‑sector testing routes such as NCSC CHECK.

CREST‑Accredited Companies

CREST‑accredited pentesting firms must undergo:

• Rigorous quality assurance audits
• Validation of internal processes
• Demonstrated the capability of their testers
• Safe data‑handling and reporting procedures

This assures clients that accredited providers deliver consistent, ethical, and high‑quality security testing.

Why CREST Matters in Pentesting

CREST has become a gold standard because it:

• Raises the bar for tester competence
• Ensures methodological consistency across engagements
• Provides buyer confidence in the quality of the pentest
• Enhances career credibility for individual testers
• Aligns with national cybersecurity schemes and regulators

CREST helps organizations avoid “low-quality pentests” that produce noise and false confidence. Instead, it focuses on defensible, repeatable, evidence‑backed results that stand up to audits or compliance reviews.

Summary

CREST brings trust, consistency, and professional rigor to penetration testing, benefiting both security professionals and organizations buying pentest services.

LAMP Server Explained: A Complete Guide to Linux, Apache, MySQL, and PHP

 What Is a LAMP Server?

A LAMP server is a classic, widely used web service stack consisting of:

Together, these technologies create a fully functional environment for hosting dynamic websites and web applications.

1. Linux – The Foundation (Operating System)

Linux is the underlying OS that provides:

• File system organization
• Permissions & user access control
• Package management
• System security
• Networking capabilities

Popular distros for LAMP servers:

• Ubuntu Server
• Debian
• CentOS / Rocky Linux
• Red Hat Enterprise Linux

Linux’s strengths include:

• Stability and uptime
• Security & permission model
• Command-line tools for automation
• Massive community support
• Cost effectiveness (usually free)

2. Apache – The Web Server

Apache HTTP Server is responsible for:

• Accepting requests from web browsers
• Processing those requests
• Serving web pages, images, scripts, and files

Key features:

Modular architecture

Modules like:

• mod_php – allows PHP to run inside Apache
• mod_ssl – enables HTTPS
• mod_rewrite – URL rewriting

Virtual hosts

Allows multiple websites on one server:

Logging

• Access logs
• Error logs

Apache is extremely flexible, stable, and widely supported.

3. MySQL (or MariaDB) – The Database Server

MySQL stores application data in relational tables.

Example use cases:

• User accounts and passwords
• Blog posts
• E-commerce products
• Session data

Core concepts:

• Databases
• Tables
• Rows/records
• Columns/fields
• Primary keys
• SQL queries

Example SQL query:

MySQL alternatives in LAMP:

• MariaDB – a drop‑in replacement created by the original MySQL developers
• Percona – optimized MySQL fork

4. PHP – The Web Programming Language

PHP runs on the server and generates dynamic HTML.

Example PHP script:

PHP is ideal for:

• Form handling
• Database interaction
• Generating dynamic content
• Server-side logic

Popular PHP applications built on LAMP:

• WordPress
• Drupal
• Joomla
• phpMyAdmin

PHP alternatives within LAMP:

• Python (Django/Flask)
• Perl

This is sometimes called LAPP or LAMP(Python).

How the LAMP Stack Works Together

Here’s the request flow:

1. Client browser sends request → https://yourserver.com

2. Apache receives the request

3. If PHP is needed → Apache hands the script to the PHP interpreter

4. PHP may request or modify data via MySQL

5. PHP generates HTML output

6. Apache sends the HTML response back to the browser

Everything happens in milliseconds.

Why LAMP Is Still Popular

Even though new stacks exist (Node.js, Docker, Nginx), LAMP remains a top choice because:

• Open source and free
• Stable and proven for decades
• Runs a huge % of web apps
• Easy to set up
• Easy to administer
• Massive community & documentation
• Works on nearly any hardware

Typical Directory Structure

Simplified Installation Example (Ubuntu)

Modern Variants of LAMP

Summary

A LAMP server is a classic and powerful web development environment combining:

• Linux – OS foundation
• Apache – Web server
• MySQL – Database
• PHP – Server-side scripting