As organizations accelerate their adoption of Generative AI (Gen AI), the trust placed in hyperscalers has never been higher. With millions of users interacting daily with AI-powered services—from copilots and chat interfaces to industry-specific automation—hyperscalers carry the responsibility of securing not just infrastructure, but the AI systems that enterprises and consumers depend on.
In this latest article in Frost & Sullivan’s AI Transformation series, we explore how Google Cloud approaches this responsibility. Our conversation with Daryl Pereira, Director & Head of “Office of the CISO” for Asia Pacific at Google Cloud, reveals a comprehensive, multi-layered strategy that reflects both Google’s decades-long security heritage and its vision for responsible AI adoption.
The Hyperscaler Mandate: Securing AI by Default
Security at hyperscale is not merely a feature—it is an obligation. According to Daryl, Google Cloud’s approach is anchored on a foundational belief:
“Securing the AI we all use starts with secure-by-design principles embedded throughout the lifecycle. You cannot bolt on security after deployment.”
This philosophy shapes Google Cloud’s four-pillar approach to responsible and secure AI.
Pillar 1: Security and Privacy by Design — Built In, Not Added On
Google Cloud’s entire AI development lifecycle is governed by secure-by-design and privacy-by-default principles. This ensures protections are not retrofitted—they are architected from the start.
This includes:
- Security reviews at every stage of AI model development
- Rigorous testing to uncover vulnerabilities within AI pipelines
- Built-in safeguards across algorithms, data flows, and deployment environments
- Continuous validation to mitigate risks such as model exploitation, data leakage, or adversarial manipulation
This proactive philosophy is core to maintaining user trust.
Pillar 2: Shaping Global Standards for Responsible AI
As AI adoption outpaces regulatory maturity, global standards are essential for establishing trust.
Google Cloud actively participates in shaping emerging AI regulations and frameworks, including:
- NIST AI Risk Management Framework (RMF)
- ISO 42001 (AI Management Systems Standard)
- European Union AI Act
This contribution helps drive industry-wide consistency in security, governance, and ethical AI use.
“AI safety cannot be achieved by any single company. It requires a collaborative ecosystem of regulators, researchers, and industry partners,”
Daryl emphasizes.
By aligning with global standards, Google Cloud ensures its guidance and offerings remain interoperable, transparent, and trusted.
Pillar 3: The Secure AI Framework (SAIF) — A Blueprint for Safe AI Adoption
One of Google Cloud’s most impactful initiatives is the Secure AI Framework (SAIF), a free, practical set of best practices designed to help all organizations—regardless of size—adopt AI responsibly.
SAIF identifies the four core AI risk domains:
- AI Models – vulnerabilities in training data, poisoning risks, unexpected model behavior
- AI Applications – threats arising from the apps and interfaces built on top of models
- AI Infrastructure – cloud, compute, network, and runtime layers powering AI
- AI Data – the integrity, sensitivity, and governance of data ingested into models
SAIF uses a risk-based approach to guide organizations in identifying, assessing, and mitigating vulnerabilities across this end-to-end AI lifecycle.
For enterprises struggling to operationalize AI security, SAIF provides a tangible, actionable roadmap.
Pillar 4: Education, Transparency, and Public Awareness
Beyond technology and frameworks, Google Cloud invests significantly in public education and thought leadership to elevate AI literacy for professionals, policymakers, and the wider ecosystem.
This includes:
- Whitepapers and practitioner guides
- Podcasts, conference participation, and public briefings
- A comprehensive curriculum on Responsible AI
- Regular engagement with regulators to inform evolving policies
“AI benefits can only be maximized when people understand the risks—and the right controls to manage them,”
Daryl notes.
These efforts reinforce Google’s broader mission: making AI safe, accessible, and understandable.
Why This Matters Now
As highlighted throughout Frost & Sullivan’s AI Transformation series, AI adoption is moving from experimentation to enterprise scale. With this shift comes a dramatic increase in:
- Data exposure and privacy risks
- Model-level and application-level attack vectors
- The complexity of monitoring AI behavior
- The need for governance that spans people, processes, and technology
Hyperscalers form the backbone of this ecosystem. Their leadership in AI security directly influences organizational trust, enterprise risk posture, and societal confidence in the technology.
Google Cloud’s multi-pronged approach—secure-by-design, global standards alignment, open frameworks, and ecosystem education—represents a model for responsible AI deployment at scale.
Frost & Sullivan’s Best Practices for Securing AI Adoption
Based on our ongoing analysis and insights from leaders like Daryl Pereira, we recommend enterprises adopt the following best practices:
- Train and Educate Your Workforce
Enable teams to understand AI risks, ethics, and safe usage practices—reducing shadow AI and misuse.
- Embed Secure-by-Design Principles
Security must be integrated into model training, deployment, workflows, and user interactions from day one—not retrofitted.
- Align with Global Frameworks
Use standards such as NIST AI RMF, ISO 42001, and local guidelines (e.g., CSA Singapore) as guardrails for your AI governance strategy.
- Conduct Full-Lifecycle Risk Assessments
Evaluate risks across models, applications, data pipelines, and infrastructure—not just user interfaces.
- Implement Enterprise-Wide AI Policies
Define usage rules, data guardrails, access controls, and audit mechanisms for staff and third-party applications.
Conclusion
AI transformation is accelerating—and so are the expectations placed on hyperscalers. Google Cloud’s efforts, as articulated by Daryl Pereira, shows how structured frameworks, secure-by-design engineering, global standards, and public education come together to build a safer AI future.
As organizations move from pilots to scale, securing AI is no longer optional—it is foundational. Enterprises that adopt robust AI governance today will unlock the full competitive and operational benefits of Gen AI tomorrow.
