Javed Hasan, CEO and Co-Founder, Lineaje – Interview Series

Javed Hasan, CEO and co-founder of Lineaje, is a veteran cybersecurity and enterprise software executive with decades of leadership experience spanning companies such as Oracle, Symantec, McAfee, and Trellix. Throughout his career, Hasan has led large-scale product, engineering, and strategy teams focused on endpoint security, cloud infrastructure, SaaS transformation, and enterprise cybersecurity innovation. At Lineaje, he is focused on addressing one of the industry’s fastest-growing challenges: securing the modern software supply chain by giving organizations visibility into the open-source and third-party components embedded inside software applications.

Lineaje is a cybersecurity company specializing in software supply chain security, helping organizations identify, secure, and manage the risks hidden inside modern software dependencies. Its platform focuses heavily on Software Bill of Materials (SBOM) technology, which acts like an ingredient list for software by cataloging every component, library, and dependency used in an application. The company provides tools for contextual risk analysis, automated vulnerability remediation, compliance management, and AI-driven “self-healing” software supply chain workflows designed to automatically identify and fix security weaknesses before deployment. Lineaje’s technology is increasingly relevant as enterprises and governments face growing threats tied to open-source vulnerabilities, software supply chain attacks, and compliance mandates surrounding SBOM transparency.

You’ve held senior leadership roles at companies like Oracle, McAfee, Symantec, and Trellix, helping shape enterprise cybersecurity products for decades. What experiences in those roles ultimately led you to co-found Lineaje in 2022, and what core problem were you determined to solve with the company?

With more than three decades in cybersecurity, I’ve built and scaled more than 50 enterprise security products, including leading Symantec’s transition to cloud with Integrated Cyber Defense Manager (ICDM) and launching one of the largest SaaS endpoint security platforms globally. Across those experiences at Oracle, McAfee, Symantec, and Trellix, I saw a consistent pattern: organizations were being asked to trust software they didn’t fully understand.

The industry had optimized for speed, but not for visibility. Open source, third-party components, automation, and now AI-generated code have made software faster to build but harder to understand. Trust became assumed instead of verified.

AI did not create this problem; it accelerated and exposed a problem that already existed. That is what led us to co-found Lineaje in 2022: to give organizations a continuous, full-lifecycle understanding of what’s in their software and now AI, where it came from, and how to govern it before it becomes a security or compliance risk.

Software supply chain attacks have become one of the fastest-growing cybersecurity threats, often spreading through open-source dependencies and third-party code. Why do traditional security tools struggle to address these risks effectively?

Traditional security tools were largely built for a different operating model. Legacy security was built for applications. Modern risk lives in ecosystems. They were designed to inspect static applications, perimeter events, or known vulnerabilities in isolation. As a result, many organizations are still operating reactively, while risk is now distributed across dependencies, build systems, package repositories, containers, transitive open-source libraries, and third-party components, often introduced long before production.

Most legacy tools lack the deep lineage, continuous visibility, and contextual understanding needed to determine whether a risky component is truly exploitable, how it entered the environment, and what it connects to downstream. This leaves organizations reacting in a landscape that increasingly demands continuous, full-lifecycle control.

Lineaje focuses on full-lifecycle software supply chain security, helping organizations understand exactly what components exist in their applications and how vulnerable they might be. Why has this level of transparency become so critical in the age of AI-generated software?

AI compresses the time between creation and exposure. It accelerates code creation without automatically increasing provenance, traceability, or trust. When developers and AI assistants can produce code and workflows at unprecedented speed, the organization still needs to know exactly which models, libraries, agents, and external services are being introduced into the environment.

Without that visibility, you cannot govern what is being built, validate compliance, and confidently ship software to customers. In today’s AI-driven world, organizations must be able to trace every dependency and model interaction, where it came from, and whether it is safe.

Lineaje is introducing UnifAI, an autonomous AI policy controller designed to govern and secure agentic AI applications at build time. What gap in the current AI development ecosystem does this product aim to address?

Enterprises are moving from AI experimentation to deploying autonomous agents across real workflows. In short, they need a security and compliance control plane for agentic AI. However, most do not yet have a central control plane to discover those AI assets, define consistent policies, and enforce security and compliance guardrails as those systems are built.

UnifAI was designed to fill that gap. It acts as an autonomous AI policy orchestrator that embeds governance directly into the development workflow. Additionally, it continuously discovers AI assets, creates an AI Bill of Materials (AI BOM), derives policies, and applies guardrails before applications reach production.

Many organizations are racing to deploy AI agents and AI-generated applications, but security teams worry about risks like prompt injection, vulnerable open-source libraries, and compliance issues. How serious are these risks today, and where are companies most exposed?

These risks are very real and serious today. Perhaps the greatest challenge with agentic AI is that the attack surface is broader and less predictable than with static software. You have prompt injection, data leakage, vulnerable open-source dependencies, and weak policy enforcement, reasoning manipulation, authorization drift, and invisible decision-making across low-code and no-code environments.

In my view, companies are most exposed because speed has outpaced governance, especially when business teams can assemble powerful AI workflows without a unified security framework, or when organizations cannot see all the models, agents, skills, and data connections operating in their environment. The system may not fail technically; it may behave correctly, but reason its way into an unsafe outcome. That is where hidden risk accumulates fastest.

One of the challenges enterprises face is balancing developer productivity with security governance. How can tools like UnifAI embed security controls into development workflows without slowing down innovation?

The right approach is to make governance operational where developers already work. UnifAI was built to integrate directly with coding assistants and low-code or no-code agentic AI platforms, so policy can be applied as applications are created rather than through manual review after the fact.

It can automatically discover assets, recommend or derive policies, translate internal governance documents into enforceable controls, and apply guardrails in the workflow itself. That means policy becomes machine-enforceable, instead of layered on. When done well, developers move faster because they are not stopping to interpret compliance from scratch, and security teams gain consistency without becoming a bottleneck.

Lineaje has been building AI-driven tools to analyze software supply chains and automatically remediate vulnerabilities. How does AI change the way organizations manage risk compared with traditional static analysis or manual security reviews?

AI changes risk management by making it continuous, contextual, and increasingly autonomous. Traditional static analysis and manual review still have value, but they are too slow and too fragmented for the scale and velocity of modern software and AI development. The goal is not more alerts. The goal is to eliminate exposure before deployment. AI can continuously map environments, correlate dependencies, assess risk in context, recommend policy, and in many cases, drive remediation automatically.

Instead of waiting for a human to discover a problem, triage it, and decide what to do next, organizations can move toward systems that identify issues earlier, understand their likely impact, and take corrective action much faster. That is the foundation of outcome-based AI security: moving from detection to prevention, and ultimately to elimination.

As AI begins generating larger portions of application code, how should organizations rethink their approach to software provenance, traceability, and trust in what they ship to customers?

Organizations need to treat provenance as a first-class requirement. In an AI-assisted development model, traceability must span the full chain of inputs, including code, open-source dependencies, models, agents, and the policies applied during development and deployment. That requires dynamic bills of materials, stronger attestation, and an operational model where trust is continuously verified rather than assumed.

The standard has to become: if you cannot trace it, govern it, and explain it, you should not be shipping it.

Regulations and compliance mandates are increasingly shaping how companies secure software and AI systems. How do you see global regulatory frameworks influencing enterprise adoption of AI governance technologies in the coming years?

Regulation will be a major accelerant. As requirements around software assurance and AI governance become more explicit, governance is becoming operational infrastructure, not a back-office compliance exercise. Enterprises will need systems that can operationalize policy rather than manage compliance through spreadsheets and point-in-time audits.

Organizations are already trying to align with emerging frameworks such as the EU AI Act and established guidance like the OWASP Top Ten for AI, but they need technology that can translate those requirements into enforceable controls inside development and runtime environments.

Over the next few years, governance platforms will move from being a nice-to-have to being part of the core enterprise control stack because regulators, customers, and boards will all expect demonstrable proof of oversight. Proof of oversight will become mandatory.

Looking ahead, what does the future of AI-driven application governance look like? Do you expect autonomous systems to eventually manage large parts of the software security lifecycle themselves?

Yes, I do believe autonomous systems will manage a much larger share of the software security lifecycle, but with human oversight focused on policy, risk tolerance, and exception handling. Security teams can no longer chase every issue across sprawling software and AI ecosystems. Governance has to operate at AI speed.

The future is a model where humans define intent and policy while autonomous systems execute continuously. Intelligent platforms will continuously discover assets, maintain live bills of materials, detect threats, enforce policy, and remediate issues in real time. Human teams will still set direction and make high-consequence decisions, but continuous governance, autonomous enforcement, and live operational trust will become the foundation. That is the only sustainable way to govern software and agentic AI at the speed organizations now expect to build.

Thank you for the great interview, readers who wish to learn more should visit Lineaje.