The discovery of CVE-2025-57771 in Roo Code AI is a reminder that AI development tools are not immune to vulnerabilities and when they sit inside the software supply chain, the risk is amplified. To ensure the security of AI tools, developers and organizations should: 1. Treat AI tools like any other third-party software – Apply vendor due diligence, patch management, and supply chain risk assessments before adoption. 2. Embed security testing into workflows – Use static/dynamic code analysis, threat modeling, and even adversarial testing specifically for AI-powered platforms. 3. Prioritize updates and monitoring – Vulnerabilities in AI tools can spread quickly across dev environments, so continuous monitoring and fast patching are critical. 4. Governance & Policy Alignment – Establish clear internal policies for how AI agents and coding assistants are used, ensuring security guardrails are part of the workflow. From my perspective in cybersecurity due diligence, the key is to balance innovation with accountability—leveraging AI to accelerate development while applying the same rigor we expect for any mission-critical tool.

A state-led approach to AI governance has both strengths and challenges. Pros: 1. States can move faster than federal bodies, tailoring policies to local industries (e.g., finance in NC, manufacturing in the Midwest, tech hubs elsewhere). 2. Encourages innovation through regional accelerators and pilot programs. 3.Creates diversity in ideas and governance models that can inform broader national standards. Cons: 1. Risk of fragmentation—businesses operating across multiple states may face a patchwork of rules, increasing compliance costs. 2. Inconsistent standards could hinder interoperability and slow down adoption. 3. Smaller states may lack the expertise or resources to build robust frameworks. From my perspective in my current role a federal baseline with room for state-level innovation would strike the best balance, ensuring consistency for businesses while allowing states to experiment with tailored approaches that foster safe innovation.

In my opinion to counter this, organizations need to adapt both their defenses and their risk management practices. From my work in cybersecurity due diligence, I see three critical areas: 1.Defense in Depth – Automated attacks exploit gaps quickly. Organizations should strengthen controls across endpoints, identity, and cloud while investing in continuous monitoring. 2.Vendor & Client Due Diligence – Many breaches happen through third parties. Building AI into due diligence—such as using agentic AI to accelerate questionnaire responses and analyzing trends in Power BI—helps organizations not only meet client demands but also spot systemic weaknesses before attackers do. 3.Human + AI Collaboration – Just as attackers are automating, defenders must combine AI-driven detection, threat intelligence, and automated testing with skilled human oversight to close the loop. The lesson - AI isn’t just powering the offense it should equally be leveraged to strengthen resilience, visibility, and risk governance.

The real driver of productivity won’t come from AI adoption alone, but from how organizations put it to work. A few key factors stand out: 1.Defined Use Cases – AI needs to solve real business problems, not just be a novelty. When tied to outcomes like reducing turnaround time, improving accuracy, or cutting costs, the value becomes measurable. 2.Seamless Integration – Productivity gains happen when AI is embedded into existing workflows and systems, so employees don’t have to constantly switch tools. 3.Employee Enablement – Training, change management, and clear guardrails are critical. Employees must know not just that AI exists, but how to use it effectively. 4.Measurement & Feedback Loops – Without metrics, AI’s impact is invisible. Organizations should track time saved, error reduction, and performance improvements to continuously refine adoption. Widespread AI use is a great start—but purposeful, integrated, and measurable AI is what will unlock true productivity gains.

One of the most promising use cases for task-specific AI agents in my industry is cybersecurity due diligence. Today, responding to client security questionnaires, RFPs, and regulatory DDQs is highly manual, repetitive, and time-consuming. Agentic AI can streamline this by: - Automating questionnaire responses using curated knowledge bases and past submissions. - Flagging gaps or inconsistencies for human review instead of requiring full manual drafting. - Integrating with Power BI to transform raw due diligence data into actionable insights and trend reports, helping leadership see patterns across client requests, risk themes, and control coverage. This shifts the process from being reactive and labor-intensive to proactive, insight-driven, and scalable, while freeing teams to focus on higher-value client engagement and strategic risk reduction.