Delivered a Handover‑Ready Security for AI Roadmap

The Client: Leading Cybersecurity Platform

Our client, a leading cybersecurity platform, connects organizations with a global community of security researchers to find and fix vulnerabilities before attackers do. As enterprises adopt LLMs, autonomous agents, and AI-powered features, the company is extending its offensive security leadership into AI systems testing — a fast-growing, under-secured attack surface spanning prompt injection, data poisoning, and unsafe agent behavior. The core of the work was building security for AI: as organizations adopt new AI tools and foundation gen AI models, their attack surface has multiplied. The new Security for AI (SAI) strategy leveraged the client’s human-led AI Red Teaming as a strategic moat and integrated it with autonomous screening into continuous, automated testing to ensure security across the whole supply chain.

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The Mission: Interim Senior PM — Security for AI Strategy & Roadmap

Reporting to the Director, Security Strategy and Operations, and the CPO, the engagement was an interim Senior PM role to initiate strategy and roadmap work before a permanent team was in place. The objective was concrete: as new AI tools multiplied the attack surface, traditional security measures could not scale as quickly and cost-efficiently. Our goal was to define a productized SAI offering that secures customers' AI systems, extending the human-led AI Red Teaming into a scalable, automated line and handing the permanent team a prioritized, validated plan.

💡 Security for AI (SAI): AI-native and HITL (Human in the loop) capabilities that continuously test AI models, apps, and agents for AI-specific failure modes (e.g., prompt injection, tool misuse, unsafe agent behavior).

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Our Main Quest: Defined the Security for AI (SAI) Vision and Roadmap

Launch: Onboarded Very Fast

In a research-heavy, security-specific domain, the role required a fast ramp-up. We aligned with primary stakeholders on a lightweight, iterative way of working, ran stakeholder interviews, and established working groups across Product, Design, and Engineering to set what "good" looked like early.

Explore and Conquer: Securing AI Systems — Discovery and Productization

Problem: As new AI tools and foundation Gen AI models multiply the attack surface — prompt injection, data poisoning, unsafe agent behavior — the client needed to turn its human-led AI Red Teaming into a productized SAI line that secures customers' AI systems, yet had no defined vision, roadmap, information architecture, or prioritized concepts. Discovery had to span several use cases, from frontier Gen AI providers to organizations just beginning to adopt AI tools, while making sure to be compliant (EU AI Act, NIST, Gartner AI TRiSM).

What we did:

• Created leadership alignment and a clear build plan by producing and validating the SAI Vision & Roadmap (2025→2027) with strategic pillars and guiding principles.
• Enabled lower-friction, self-serve activation by defining a new Information Architecture and a Security Journey (Onboarding) Wizard concept.
• Scoped a Baseline Scanner concept off the EASM (External Attack Surface Management) scanner foundation, plus tactical PRDs for handover.
• Kept the roadmap buildable (not theoretical) by running prioritization and technical feasibility checks with Engineering.

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Mini-Missions: Our Side Quests

• Findings & Recommendations Report: Improved team velocity and cross-team visibility by identifying operational bottlenecks and delivering prioritized recommendations.
• Competitor & market research: Mapped the emerging AI security landscape to ground the roadmap and target customer profiles (Emerging AI Adopters, Scaling AI Deployers, Advanced AI Enterprises).
• Compliance framing: Tied SAI capabilities to EU AI Act, NIST, and Gartner AI TRiSM expectations to strengthen enterprise credibility.

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Mission Achievements: Delivered Outcomes

Enabled the incoming team to execute immediately by delivering a handover-ready, leadership-validated SAI Vision & Roadmap (2025→2027).

Accelerated 0-to-1 execution by translating an emerging opportunity space into prioritized, buildable concepts — PRDs, concept documents, and prototypes ready for the permanent team.

Improved cross-team alignment and reduced duplicated effort by identifying overlap between Baseline Scanner and EASM (External Attack Surface Management) early and establishing cross-team syncs.

Reduced manual effort and improved cross-team visibility by creating a Findings & Recommendations Report that surfaced operational risks and prioritized recommendations.