In the 1990s, middleware was the muscle that made enterprise computing truly scalable. Before middleware, applications communicated with databases and back-end services directly using drivers like ODBC (Open Database Connectivity) or native SQL calls. This was simple but brittle, used shared secrets, and riddled with risks. It required developers to write custom code for every application and made scaling client-server architectures an art form rather than a best practice in architectures. Then came the explosion of middleware products: CORBA, COM/DCOM, MQSeries, Tibco, and a slew of other brokers that abstracted connectivity, complexity, pooled connections, balanced loads, and transformed the enterprise into a library of reusable services. Middleware became the data center component that let businesses scale, integrate, and innovate without rewriting every application from scratch and without direct database connectivity from the client.
Fast-forward three decades, and we are at a similar inflection point. This time, it is not about databases and message queues, it is about Artificial Intelligence (AI) becoming the new middleware to communicate with the very same databases and applications we have been developing and reinventing for the last 30 years.
AI is the New Middleware
Just as middleware once sat between applications and infrastructure, AI now sits between humans, systems, and data. AI is no longer just a tool to answer queries or generate text; it is acting as the orchestration layer for decision-making and data correlation. Agentic AI, in particular, does not just respond to queries, it brokers actions and responses. It can ingest context, scape data from other applications interfaces, normalize inputs from multiple systems, apply embedded logic, and then execute on behalf of the user.
Where middleware once converted proprietary database calls into standardized ODBC queries, AI now converts natural language into API calls, workflow instructions, or even infrastructure changes. It is the broker between intent and execution, freeing developers and users from the need to directly interface with every underlying system. Today, we see this in the rapid adoption and evolution of MCP Servers becoming the foundation of AI middleware.
Complexity Obfuscated by Simplicity
Middleware thrived because it abstracted complexity. Developers could write a single piece of code and let middleware handle connection pooling, retries, message formatting, authentication, authorization, and load balancing. In the same way, AI abstracts the complexity of interacting with diverse resources and normalizes the output in human readable forms in lieu of XML and database fields. Need proof, think of this simple example: scheduling a meeting. In the 1990s, this meant connecting to an Exchange server API or writing MAPI calls. Today, an AI assistant can parse an email, identify intent, negotiate times with other calendars, and schedule the event for everyone without a developer coding to dedicated workflow. AI is shielding layers of complexity behind a conversational or task-driven interface, making technology feel human-friendly while it does the heavy lifting in the background. This mirrors the promise of middleware: interoperability without constant reinvention is now being applied to AI.
Risks of AI as Middleware
However, the lesson from the 1990s is clear: middleware also created single points of failure, dynamic attack surfaces, and potential lateral movement for threat actors. When middleware brokers were compromised, every connected system was at risk. Today, AI introduces an even bigger challenge to the old middleware paradigm. AI as middleware does not just process information, it decides what to do with it and can hallucinate, create confused deputies, or be manipulated to disclose sensitive information.
Security and governance must catch up with the hype cycle of AI implementations. Just as we needed identity and access controls for database connections and message brokers, we now need identity security, least privilege, and just-in-time access for AI. If an AI “middleware” layer can initiate privileged actions like reset passwords, create users, or modify infrastructure, then it must operate under Zero Trust principles. Its authentication must be strong, its authorization granular, its activity auditable, and its behavior monitored.
This is why the conversation around AI security is really a conversation about identity security. Agentic AI cannot be given the keys to the kingdom without guardrails, or we risk creating the ultimate lateral-movement platform for threat actors based on an immature security model for AI implementations, especially as a new middleware component.
Productivity vs. Control
Like the middleware boom, AI as middleware will be a productivity revolution. Enterprises will unlock efficiencies as AI brokers repetitive tasks, integrates legacy systems, and drives faster decision-making. Legacy applications that still rely on direct SQL drivers or proprietary protocols can be wrapped with AI-driven interfaces, effectively extending their lifespan and usability and potentially create vast new use cases based on the capabilities of AI datamining.
However, all organizations should pause for a moment and weigh productivity against control. Middleware in the 1990s eventually evolved into enterprise service buses and SaaS solutions. AI will require its own governance maturity: policies to define what actions AI can take autonomously, how human-in-the-loop oversight works, and how we measure trustworthiness and security risks. This is best illustrated in the table comparing middleware attributes from the 1990s to AI today:
| Concept | 1990s Middleware | AI Middleware |
|---|---|---|
| Connectivity | CORBA, MQ, ODBC, SQL | LLMs, MCP Servers, Agentic AI |
| Primary Function | Abstracted database and service connections from applications | Abstracts intent into actions, brokers decisions, automate responses |
| Interface | API calls, message brokers, RPCs | Natural language, embeddings, API orchestration |
| Complexity Obfuscation | Connection pooling, load balancing, message routing | Multi-system integration, reasoning, autonomous execution |
| Risk | Centralized point of failure, DoS, message replay | Over-permissioned AI, prompt injection, decision errors, lateral movement, privileged escalation |
| Security Need | Identity for service accounts, network ACLs | Identity security, least privilege, JIT access, Zero Trust, privileged access management |
| Business Value | Scalable client-server architectures | Autonomous, human-like productivity across all systems, scalable |
| Governance Evolution | ESB, SOA governance frameworks | AI safety guardrails, human-in-the-loop oversight, data leak protection |
Tomorrow’s AI Enterprise Fabric
In a sense, AI is middleware 2.0 but with unprecedented autonomy. It is not just asking a question; it is deciding what the answer most likely is and formatting it into human readable format or machine / application digestible content. This is both thrilling and terrifying. Middleware never woke up one morning and decided to reconfigure your firewall, change your thermostat, or rebook your flights. AI can.
Forward-looking organizations should treat AI like they treated middleware in its infancy: secure by design, experiment, adopt, and introduce into production with full lifecycle management. And consider identity security to embrace AI services for governance, monitoring process activity, and design kill-switches for when (not if) AI makes a mistake. It probably will.
Conclusion
Just as the 1990s middleware wave made distributed computing possible, the AI middleware wave will make autonomous computing practical. If history teaches us anything, it is that every new layer of abstraction creates both opportunity and risk. AI is not just an application feature but rather a new integration layer, a new decision fabric, and the possibilities and risks are more than can be consumed in this simple byline. And just like its 1990s predecessor, it will be indispensable, invisible, and, if not properly governed, incredibly dangerous. The organizations that recognize AI’s role as middleware and secure it accordingly will be the ones that thrive this next technology evolution.
About the Author: Morey J. Haber is the Chief Security Advisor and lead identity and technical evangelist at BeyondTrust. He has more than 25 years of IT industry experience and has authored five books in the Attack Vectors series. He previously served as BeyondTrust’s CISO, CTO, and VP of Product Management.
About the Organization: BeyondTrust is the global identity security leader protecting Paths to Privilege™. Our identity-centric approach goes beyond securing privileges and access, empowering organizations with the most effective solution to manage the entire identity attack surface and neutralize threats, whether from external attacks or insiders. BeyondTrust is leading the charge in transforming identity security to prevent breaches and limit the blast radius of attacks, while creating a superior customer experience and operational efficiencies. We are trusted by 20,000 customers, including 75 of the Fortune 100, and our global ecosystem of partners.
Learn more at www.beyondtrust.com.
