Tag Archives: machine-learning

Cybersecurity in Industrial systems with AI

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AI is transforming not only digital platforms but also industrial systems. As AI intersects with cybersecurity, how do we protect our infrastructure while adapting to technological changes? This rapid evolution brings both new opportunities and risks, increasing the need for robust security strategies. Balancing innovation with critical safeguards will be essential as organizations navigate this complex landscape.

Information Technology and Operational Technology

When working with industrial systems, it is important to distinguish between two key areas:

  • Information Technology
  • Operational Technology

Information Technology: This area focuses on data, information, and communication. Key aspects include data storage, transmission, and analysis. In terms of cybersecurity, the primary concerns are:

  • Confidentiality (protecting data)
  • Integrity (ensuring accuracy)
  • Availability (keeping systems operational)

Examples of solutions in this category include productivity suites, ERP applications, cloud services, databases, and CRM systems.

Operational Technology: These technologies are designed to monitor and control physical processes, devices, and infrastructure. The main objectives are: real-time monitoring, control, automation, and ensuring the safety and reliability of operations. Priority areas include:

  • Safety (preventing harm to people, environment, and equipment)
  • Availability (maintaining continuous system operation)
  • Determinism (achieving predictable outcomes)

Examples of operational technology solutions include:

  • Programmable Logic Controller (PLC): Computers used to automate industrial processes, such as assembly line robots
  • Supervisory Control and Data Acquisition (SCADA): Systems for remote monitoring and control of industrial processes
  • Distributed Control System (DCS): Control systems where elements are distributed across the system rather than centralized, often used in chemical plants and refineries (e.g., carbon capture systems)

Where does AI add value to Operational Technologies?

Industrial Systems

Most of the industrial systems use legacy protocols (e.g., Modbus, DNP3, etc.); these were designed for availability and determinism, not for security. This is where AI can add value.

  • Anomaly detection and Predictive Maintenance: AI models can learn “normal” patterns of sensors, actuators, and control data and flag deviations that indicate equipment wear, sensor drift, or cyber manipulation
  • Cyber Intrusion Detection for OT Networks: AI can profile normal Modbus and DNP3 traffic and flag malicious commands such as replay attacks or unauthorized writes to PLCs. As many of these protocols lack authentication or basic identity management
  • Process optimization: Reinforcement learning agents can continuously optimize SCADA-controlled processes (e.g., water treatment plants) for throughput, yield, or energy efficiency
  • Human-in-the-Loop decision support: Agents that can interpret signals and alarms and recommend operator actions that reduce “alarm fatigue”

Driverless cars

The development of robotaxis is a major advance in autonomous transportation. These driverless vehicles function as multi-agent industrial systems, where addressing security concerns is important to prevent potential issues.

  • Perception and Sensor Fusion: AI combines information from cameras, LIDAR, radar, and V2X to construct an environmental model, such as proximity maps used in vehicles like Tesla.
  • Real-time Anomaly Detection and Intrusion: Systems are designed to identify LIDAR spoofing or harmful V2X messages, with agents monitoring Ethernet frames for irregularities.
  • Risk Forecasting and Path Planning: Driving policies are automatically adapted based on the predicted movements of vehicles and pedestrians.
  • Self-Diagnostics and Predictive Maintenance: Onboard agents monitor for sensor and board failures, enabling proactive recalls to reduce operational expenses.
  • Over-the-Air (OTA) Update Security: AI assists in verifying firmware integrity and identifying any supply-chain tampering.

Protocol security gaps

Many industrial and automotive controls lack built-in security, so AI can help compensate for vulnerabilities in legacy protocols.

  • AI-driven intrusion detection: Identifies and contains unusual or malicious traffic by analyzing patterns.
  • Device behavioral fingerprinting: Uses electrical and timing signatures to reliably distinguish devices, preventing impersonation.
  • Zero-trust enforcement: Dynamically assesses communication trust for insecure protocols using AI.

Conclusion

In summary, the integration of AI into automotive and industrial systems significantly enhances security, operational reliability, and adaptability. By leveraging advanced perception, real-time anomaly detection, predictive maintenance, and dynamic trust enforcement, AI fills gaps in legacy protocols and sets a new standard for proactive threat mitigation and system resilience. As these technologies continue to evolve, their role in safeguarding critical infrastructure will become increasingly indispensable for the future of connected and autonomous systems.

Identity in a Multi-Agentic world

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Overview

As artificial intelligence and automation evolve, we are entering a multi-agentic world. Multi-agentic implies a distributed environment where autonomous software agents, APIs, machine learning models, and human users act in concert. Identity is no longer a technical detail; it is a core requirement for system integrity, trust, and control.

Agents today write code, deploy infrastructure, triage support tickets, summarize meetings, and in some cases make decisions. How do you know who made the decision and what liability your organization faces when agents collaborate, orchestrate, and reason?

You can’t coordinate a system that you cannot trust, and trust starts with a core capability: identity!

Identity is not for users only

Single sign-on, multi-factor authentication, and directory sync are basic requirements for user identity. With hundreds or even thousands of non-human agents like retrieval bots, security assessments, AI code reviewers, and autonomous workflows active at any given time, these measures are essential.

In large organizations, understanding the workflow, accountability, and permissible actions is crucial. Identity plays a fundamental role in these situations.

The issue affects everyone. It represents a change in control systems. Identity isn’t just about who logs in; it’s about making secure, transparent decisions and taking actions on a large scale.

Why is identity core to multi-agentic systems?

Identity is crucial to multi-agent systems. Here are six important reasons why.

🔐 Trust and Authentication

In a decentralized agent ecosystem, it’s important for us to understand who or what we are interacting with.

Is this code review coming from an approved AI agent or a spoofed script? Is it a rogue bot or an authorized user that initiates the workflow?

We can’t rely on IP addresses or client secrets anymore. We need signed, verifiable agent identities that persist across time and context.

🧾 Auditability and Accountability

Without identity, there’s no provenance.

When agents approve purchases, modify infrastructure, or triage incidents, it is essential to maintain a complete and tamper-proof record of the actions taken:

  • Who acted
  • On whose behalf
  • Under what authorization
  • With what outcome

This isn’t just good practice. It is essential for security, compliance, and debugging in enterprise systems.

Remember the old Abbott and Costello routine on “Who’s on first?” Who knew that comedy routine was so prescient!

👥 3. Delegation and Agent Chaining

In human teams, we delegate work to others. In multi-agent systems, delegation becomes the norm.

For example:

  • A user asks their assistant to generate a report
  • The assistant calls a forecasting agent
  • The forecasting agent queries a data governance agent to ensure compliance

At every step, the original identity and permissions need to be preserved. We must know who initiated the action and whether each agent is authorized to act on their behalf.

🔐 4. Fine-Grained Authorization

Multi-agent systems don’t work if every agent has God mode.

Each agent needs just enough access to do its job and no more.

This means:

  • Identity-linked roles and scopes
  • Time-boxed or task-limited permissions
  • Attribute-based access policies that adjust dynamically

Without strong identity-linked authorization, we’re just building smarter ways to breach ourselves.

🧠 5. Personalization and Adaptation

Good agents don’t just act; they learn.

But learning requires context:

  • What team is this user on?
  • What systems do they interact with?
  • What are their preferences?

Identity is the gateway to this context. It allows agents to personalize their behavior, become more useful over time, and avoid making dumb, default assumptions.

🌐 6. Interoperability Across Ecosystems

As agents start to collaborate across platforms, e.g., your Jira assistant talking to a GitHub bot or a Salesforce AI; the need for interoperable identity becomes critical.

That’s where standards like OIDC, SCIM, and even emerging ideas like Decentralized Identity (DID) and Verifiable Credentials (VCs) come into play.

Imagine an agent from Microsoft Graph collaborating with one from Atlassian Forge is only possible if identity flows freely but securely between them.

What Happens When You Don’t Prioritize Identity?

Without a strong identity layer, you get:

  • Shadow agents making untraceable decisions
  • Permission creep where every agent can do everything
  • Cross-system silos that break orchestration
  • Unverifiable outputs from unknown actors

It’s a recipe for disaster in regulated, security-sensitive, or high-trust environments.

Identity Is the New Control Plane

In human organizations, identity governs org charts, responsibilities, and roles.

In a multi-agent system, identity governs logic, execution, and autonomy.

Here’s where we need to invest:

  • Agent Identity Lifecycle – Issue, rotate, revoke agent credentials
  • Delegation Frameworks – Secure “on-behalf-of” interactions
  • Observability Tied to Identity – Logs, metrics, and decision trees with clear attribution
  • Policy-as-Code for Access – Role and attribute-based access enforcement
  • Cross-Domain Trust – Federated or decentralized identity for external agent collaboration

Closing Thoughts

We’re moving toward a world where autonomous agents are as common as microservices—and far more powerful. But without a robust identity layer, these systems will be fragile, opaque, and untrustworthy.

As a product leader, I see it clearly: identity is no longer a backend feature. It’s an architectural foundation for the next generation of intelligent systems.

If you’re building for the future, start by asking:

“Can I trust the agents in my system?”

And then:

“Can I prove it?”

In a multi-agent world, identity serves as the foundation for all trust.

Let’s connect: If you’re working on agent frameworks, trust layers, or identity models, I’d love to trade notes. The agentic future is here, let’s build it right.

The layers of Artificial Intelligence

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I have started blogging again, and it feels great to be back! It’s an exciting time to jump in, especially with all the developments in AI (Artificial Intelligence). I am really excited because I believe that during our lives, we will be able to find a cure for cancer and tackle climate change globally with AI.

So, what makes AI systems, like chatbots, recommendation tools, or even autonomous vehicles work? The answer is layers. What do I mean by layers? To make AI work, there are many layers involved. These layers are the hidden heroes behind all the amazing things we can achieve with Artificial Intelligence. Let’s explore what makes them brilliant!

1. The Infrastructure Layer

The infrastructure layer is essential for any AI system, providing the computing power and storage to manage large data and complex tasks. Think of it as the oven and tools needed for baking a cake. Key components include cloud platforms, GPUs, and powerful servers. Without this layer, AI systems lack the strength to operate. Besides the main infrastructure, there are also important aspects like security, compliance, identity, scaling, and backup and recovery timelines.

2. The Data Layer 

Data is the raw ingredient for AI—like the flour, sugar, and eggs for your cake. The data layer involves collecting, storing, and processing data. It ensures that the data is clean, organized, and accessible for further use. Databases, data lakes, and data pipelines play a crucial role in this layer, ensuring your AI system has a steady supply of high-quality “ingredients.” 

3. The Model Layer 

Moving on, the model layer is where the real magic happens. This layer involves training and fine-tuning AI models to perform specific tasks, such as recognizing images, understanding speech, or predicting trends. Think of this as mixing and baking your ingredients into a delicious cake. Machine learning algorithms and frameworks like TensorFlow or PyTorch are the key tools in this layer. 

4. The Orchestration Layer 

This is the conductor of the AI symphony. The orchestration layer ensures that all the other layers work in harmony. It manages workflows, integrates components, and ensures scalability and efficiency. Imagine this as the recipe book and timer that guide you through the baking process. Without orchestration, the entire system can become chaotic and inefficient. 

5. The Application Layer 

Finally, the application layer is where AI meets the real world. This is the beautifully decorated cake that everyone gets to enjoy. It includes user interfaces, APIs, and AI-powered applications, such as chatbots, recommendation systems, or autonomous vehicles. This layer ensures that the end-user can interact with and benefit from the AI system effortlessly. 

Conclusion 

In summary, the layers of AI work together like a well-baked cake, with the infrastructure, data, model, orchestration, and application layers playing their distinct roles. As the orchestration layer brings harmony to the entire stack, it ensures that all components collaborate seamlessly to deliver intelligent and efficient solutions. Understanding these layers is the first step toward appreciating the brilliance behind AI systems!