In the rapidly evolving world of cybersecurity and AI, few voices carry as much weight as Malik Haidar. With years of experience safeguarding multinational corporations from sophisticated threats, Malik has become a trusted expert in analytics, intelligence, and security. His unique ability to weave business priorities into robust cybersecurity strategies has made him a go-to advisor for organizations navigating the complexities of AI governance. In this interview, we dive into the challenges of securing AI in enterprise environments, explore the delicate balance between innovation and risk, and uncover practical approaches to creating governance that works in the real world. Join us as Malik shares his insights on building sustainable AI strategies that empower businesses without compromising security.
Can you explain why AI governance has become such a pressing challenge for CISOs in today’s landscape?
Absolutely. AI governance is a massive challenge because AI isn’t just another tool—it’s a transformative force that’s being adopted at an unprecedented pace across industries. Unlike other technologies, AI systems often operate as black boxes, making it hard to predict or control their outcomes. For CISOs, this creates a unique set of risks, from data leaks in prompts to regulatory blind spots. On top of that, the stakes are incredibly high. A single misstep can lead to breaches or compliance failures, while overreacting with heavy-handed restrictions can stifle innovation. It’s a tightrope walk that demands a deep understanding of both the tech and the business context.
What sets AI apart from other emerging technologies when it comes to crafting effective governance?
AI stands out because of its complexity and adaptability. Unlike, say, cloud computing or IoT, where risks are often tied to specific infrastructure or devices, AI’s risks are embedded in algorithms, data inputs, and decision-making processes that can evolve on their own. This means governance can’t just be a static checklist; it has to account for systems that learn and change over time. Additionally, AI often gets woven into existing tools and platforms without clear visibility, so CISOs are sometimes playing catch-up just to understand where it’s being used. That lack of transparency makes traditional governance models fall short.
How does the rapid pace of AI adoption affect the ability to implement solid governance frameworks?
The speed of AI adoption is a double-edged sword. On one hand, it drives incredible efficiency and innovation—businesses can’t wait to leverage it. On the other, it leaves little time for CISOs to build thoughtful controls. When employees start using AI tools overnight, often without approval, shadow AI becomes a real problem. Governance frameworks take time to design, test, and roll out, but the technology doesn’t wait. This mismatch often leads to reactive policies that either fail to address real risks or get ignored because they’re out of touch with how people actually work. It’s a constant race to keep up.
Why do rigid AI policies so often fail to deliver the intended results?
Rigid policies fail because they’re usually written in a vacuum, without considering the messy reality of how organizations operate. They might look comprehensive on paper, but they don’t account for the speed of change in AI or the way employees naturally adopt tools to solve problems. When policies are too strict or disconnected from daily workflows, people find workarounds, which often means using unapproved tools that expose the company to risk. Plus, AI evolves so fast that a policy written today might be obsolete in six months. Without flexibility and real-world input, these rules become more of a liability than a safeguard.
How can CISOs strike a balance between securing AI systems and fostering innovation within their organizations?
Striking that balance starts with shifting the mindset from being the ‘department of no’ to a partner in progress. CISOs need to understand the business goals driving AI adoption and map security measures to those priorities. It’s about creating guardrails that protect without slowing things down—like providing approved, enterprise-grade AI tools so employees don’t resort to insecure alternatives. Communication is key; engaging with business units to understand their needs and risks helps tailor governance that’s practical. Ultimately, it’s about enabling safe transformation, not blocking it, while aligning with the organization’s risk appetite.
What are the dangers of adopting AI too quickly without proper safeguards in place?
Moving too fast without safeguards is a recipe for disaster. One of the biggest dangers is data exposure—think sensitive customer info or proprietary data being fed into AI prompts that aren’t secure. Then there’s the risk of shadow AI, where unvetted tools proliferate because employees don’t have approved options. Regulatory compliance is another minefield; without proper controls, you could violate data privacy laws or industry standards, leading to fines or reputational damage. These missteps don’t just create technical problems—they can erode trust and put the entire business at risk.
What happens when a company moves too slowly in adopting AI compared to its competitors?
If a company drags its feet on AI, it risks falling behind in a big way. Competitors who adopt AI effectively can achieve game-changing efficiencies—faster decision-making, better customer experiences, lower costs—that are hard to match. This isn’t just about losing market share; it can mean losing talent, too, as employees want to work with cutting-edge tech. For CISOs, the pressure mounts because leadership might blame security for holding things back. Being overly cautious can cost as much as being reckless, just in different ways, often impacting the company’s long-term viability.
Can you elaborate on what a ‘real-world forward’ approach to AI governance means in practice?
A ‘real-world forward’ approach means designing governance based on what’s actually happening within the organization, not just theoretical risks. It’s about getting out of the boardroom and into the trenches—understanding how employees use AI, which tools they rely on, and where it’s embedded in workflows. This requires CISOs to gather data on usage patterns and engage with teams across departments. From there, you build policies that address real behaviors and risks, not just ideals. It’s a dynamic process, constantly adapting to new use cases and tech developments, ensuring governance stays relevant.
How can CISOs gain visibility into how AI is being used by employees on a day-to-day basis?
Gaining visibility starts with open dialogue—talking to employees and business units about their workflows and the tools they’re using. Surveys or informal check-ins can reveal a lot about shadow AI or unofficial adoption. Beyond that, tools like AI inventories help map out where AI is deployed across systems and applications. Monitoring software usage through IT systems can also flag unapproved tools. The goal isn’t to spy but to understand the reality on the ground. Partnering with IT and HR to track adoption trends ensures you’re not missing critical blind spots.
Why is it so important to know where AI is embedded in tools and platforms across an organization?
Knowing where AI is embedded is critical because it’s often hidden in plain sight. Many SaaS platforms or productivity tools now integrate AI features without explicitly advertising them, and employees might not even realize they’re using AI. If CISOs don’t have a clear picture, they can’t assess risks like data exposure or compliance issues tied to those tools. Mapping out these integrations helps identify vulnerabilities and ensures governance covers the full scope of AI usage. Without this, you’re essentially governing in the dark, which is a dangerous place to be.
How do tools like AI inventories and model registries contribute to stronger governance?
AI inventories and model registries are game-changers for governance because they bring clarity to chaos. An AI inventory gives you a comprehensive view of all AI systems and tools in use, so you know what’s out there and where risks might lie. Model registries take it a step further by tracking specific AI models—when they were deployed, how they’re performing, and whether they need updates or decommissioning. This prevents ‘black box sprawl,’ where unmonitored models create hidden risks. Together, these tools help CISOs make informed decisions and maintain control over a complex landscape.
What role do cross-functional AI committees play in making governance a shared effort?
Cross-functional AI committees are essential because governance can’t just be a security or IT problem—it impacts the whole organization. These groups bring together folks from legal, compliance, HR, and business units to ensure diverse perspectives shape policies. This isn’t just about spreading the workload; it’s about aligning governance with business outcomes and making sure rules are practical across departments. When everyone has a stake in the process, compliance becomes a shared goal, not just a mandate from security. It builds buy-in and reduces friction.
What lessons can we draw from situations where blanket bans on AI tools fail to keep pace with organizational needs?
Blanket bans often backfire because they ignore the reality of how fast organizations and technology move. The key lesson is that prohibition without alternatives doesn’t work—employees will find ways around restrictions, often using unapproved tools that create bigger risks like shadow AI. It also shows the importance of adaptability; policies need to evolve with leadership changes and tech trends. CISOs should focus on enabling safe usage through approved tools and clear guidelines, rather than trying to stop adoption altogether. It’s about guiding behavior, not dictating it.
What is your forecast for the future of AI governance in enterprise environments?
I think AI governance will become a cornerstone of enterprise strategy over the next few years, as AI embeds itself deeper into every aspect of business. We’ll see more standardized frameworks emerge, driven by regulation and industry collaboration, to help CISOs navigate risks consistently. At the same time, governance will need to become more automated—think AI-driven monitoring and compliance tools—to keep up with the scale and speed of adoption. The challenge will be ensuring these systems remain human-centric, balancing tech with accountability. Ultimately, I believe successful governance will be what separates thriving businesses from those left behind in the AI era.
