Few technologies have gained so much traction within businesses quite so fast as artificial intelligence (AI). According to IDC, the past two years have been marked by the rising enthusiasm of business executives for the opportunities and promises of generative AI (GenAI) to reimagine their businesses and operating models and drive sustainable business value. But the growth of AI also comes with some significant risks – particularly around bias, hallucinations and privacy compliance. In fact, research from IDC reveals that 99% of CEOs in EMEA have identified “using AI responsibly” as a paramount priority for their organisations.1
Foundry reached out to the CIO Experts Network, a community of IT professionals and technology industry influencers, to discuss the guardrails needed to ensure AI is deployed responsibly.
Prioritising good data governance
Ramprakash Ramamoorthy, Head of AI Research at Zoho Corporation believes that “IT leaders must implement comprehensive data governance frameworks that emphasise transparency, security, and compliance with global privacy regulations. As AI systems rely heavily on vast and sensitive datasets, it’s essential to mitigate risks such as data leakage, model bias, and adversarial attacks.”
Kieran Gilmurray, Chief AI Innovator at Technology Transformation Group, agrees: “AI tools are only as effective as the quality and security of their data so IT leaders must take clear, deliberate action. It’s essential to control access to sensitive information, secure internal AI systems, and prevent confidential data from falling into the wrong hands. By conducting regular assessments and implementing strong encryption measures, leaders can maintain trust and ensure strict compliance with regulations.”
Other experts agree on the vital importance of data governance for AI applications. Daniel Jacobs, Founder & CEO at Starkhorn, believes that good data governance starts at the top of the enterprise. “IT leaders play a crucial role in enhancing organisational security by implementing robust data governance practices, establishing strong access controls, and leveraging AI-driven threat detection to proactively identify potential risks,” he says. “By conducting regular audits and providing ongoing employee training, they can ensure that security protocols evolve in response to emerging threats, ultimately safeguarding sensitive information in ever-changing environments. This proactive approach fosters a culture of security awareness and resilience within the organisation.”
Vendors are helping their enterprise customers meet this data governance imperative head on. AMD, for instance, has created a Responsible AI framework, which reflects the company’s commitment to protecting users’ confidential information and maintaining data privacy. The framework addresses such issues as fairness, inclusion, transparency, reliability, and the safety of AI systems, and aims to preserve human agency through human-centric AI design.
On-device controls are an important layer of AI security, helping to protect data, strengthen compliance, and maintain privacy even while the data is actively being processed. One example is confidential computing, which uses hardware-based protections to keep information secure in use, shielding it even from privileged system software. AMD applies this approach in its processors, where confidential computing is built into the silicon as part of AMD Infinity Guard, a suite of security features that help maintain data security and privacy while workloads are in use.
Layered security for enhanced protection
According to technology experts, enterprises will need to adopt a wide range of measures to protect their data and systems in this new era of AI. As Ramamoorthy explains: “Incorporating AI-specific monitoring tools and encryption protocols helps ensure data integrity throughout the lifecycle, as well as ongoing employee training and ethical oversight which are also critical to maintaining responsible data practices. Protecting data in the AI era requires a proactive, multi-layered approach that evolves with the technology.”
For Tom Allen, founder of the AI Journal, a multi-layered approach to data security means “combining robust encryption, strict access controls, and real-time monitoring to safeguard sensitive information at every touchpoint. Equally important is the establishment of clear data governance policies and a culture of privacy awareness, ensuring that every team member understands their role in protecting data and complying with regulations.”
Allen provides an example from the healthcare industry.
“By leveraging closed AI systems and conducting regular audits, healthcare providers can maintain compliance with regulations like HIPAA [the Health Insurance Portability and Accountability Act], protect patient trust, and enable innovation without compromising privacy,” he says.
Protecting data in the age of agentic AI
A significant complicating factor in the responsible and secure deployment of AI is the introduction of agentic AI models. Agentic AI tools are artificial intelligence systems that can make autonomous decisions, set goals, and take actions to achieve those goals without constant human oversight.
These systems exhibit a degree of independence and initiative and even have the potential to adapt their behaviour based on changing environments or objectives.
Javier Campos, AI Safety Researcher and Group Chief Technology Officer at Peach, outlines some of the security challenges that come with Agentic AI: “In an era where agentic AI autonomously handles tasks across ecosystems, IT leaders must implement decentralised data governance frameworks to secure sensitive information processed by distributed AI agents, countering risks like data leakage and adversarial manipulation.”
According to Campos, advanced privacy-preserving techniques, such as federated learning and homomorphic encryption, are critical to protect data while enabling AI functionality, alongside real-time behavioural monitoring to detect agent-specific threats like prompt injection or model inversion. “Cross-functional AI security teams must integrate legal, ethical, and technical expertise to enforce strict data minimisation and anonymisation, ensuring compliance with global privacy regulations. Proactive adversarial training and secure multi-party computation will safeguard against evolving threats, preserving trust in AI-driven environments,” he adds.
As organisations advance their AI adoption and integrate increasingly sophisticated agentic applications, choosing the right technology partner becomes critical. AMD stands out in this respect through end-to-end security features that extend from the data centre to the endpoint, using hardware-based protections included as part of AMD Infinity Guard.
Combined with a rich ecosystem of partners and a commitment to open, scalable solutions, AMD empowers enterprises to innovate with confidence. By embedding security at the silicon level and supporting secure virtualisation and workload isolation, AMD helps ensure that AI-driven innovation takes place alongside robust data integrity, security, and regulatory compliance.
1 IDC, “EMEA Leaders in the Age of AI Everywhere,” November 2024