Securing AI: A CISO’s Perspective on Trust and Resilience

Artificial Intelligence (AI) has quickly moved from a promising technology to an operational reality. From automating routine processes to making sense of complex datasets, AI is changing how organizations operate and compete. But with this new power comes a new set of risks—and as security leaders, we can’t afford to treat AI like just another tool in the stack.

At its core, AI is about enabling machines to sense, learn, and act in ways that mimic or even augment human capabilities. It has the potential to turn average processes into exceptional ones, and in some cases, deliver insights humans could never achieve on their own. But every CISO knows: what can be used for good can also be abused.

I’ve seen this play out first-hand. In one industry example, fast-food chains adopted AI-driven ordering systems to streamline customer experience and reduce costs. Unfortunately, security wasn’t built in from the start. The result? Fraudulent orders, operational losses, and ultimately, a pause on AI adoption while strategies were recalibrated. It’s a sobering reminder that AI without security isn’t innovation—it’s exposure.

The New Security Mandate

Securing AI is not just about protecting infrastructure—it’s about protecting trust. If customers, employees, or regulators lose confidence in how we’re using AI, the damage can outweigh any operational gains. Here’s how I break down the AI security mandate:

1. Protect the Data

Data is the lifeblood of AI. Strong encryption, anonymization, and role-based access controls aren’t optional—they’re table stakes. If the data pipeline isn’t secure, the model can’t be trusted.

2. Protect the Models

Adversarial manipulation of AI is not theoretical; it’s already happening. Harden models against adversarial attacks, embed watermarking to detect misuse, and ensure deployments are secured and monitored like any other critical workload.

3. Secure the AI Supply Chain

Just as we’ve learned with software supply chain attacks, we must verify the provenance of training data, model weights, and ML frameworks. Third-party models and APIs need rigorous security audits before they touch our environments.

4. Monitor, Test, and Adapt

Operational resilience requires logging, monitoring, and rate-limiting AI interactions. Regular red-teaming and adversarial testing should be part of ongoing risk assessments—not a one-time event.

5. Build in Governance and Ethics

Bias, explainability, and transparency aren’t just ethical concerns—they’re regulatory and reputational risks. As CISOs, we need to work with legal and compliance teams to ensure responsible AI usage policies are defined and enforced.

6. Keep Humans in the Loop

AI should augment—not replace—human decision-making, especially in high-risk scenarios. Approval gates and feedback loops are essential to balance automation with accountability.

7. Plan for Failure

Resilience means assuming things will go wrong. Immutable backups, model rollback capabilities, and incident response playbooks for AI-specific threats (poisoning, data leakage, prompt injection) are no longer nice-to-have.

 

Leading with Security

AI has the power to make organizations more efficient, more innovative, and more resilient. But only if we build it on a foundation of security and trust. As CISOs, it’s our responsibility to ensure that AI adoption doesn’t outpace our ability to govern and secure it.

The lesson is simple: AI should enhance human capability, not replace it—and certainly not compromise it. Done right, securing AI isn’t just about mitigating risk. It’s about enabling trust, accelerating innovation, and ensuring AI becomes a force multiplier for the business, not a liability.