Page 16 - Paramount PR Report - April 2025

AI's full force: secured
Regulatory scrutiny is also intensifying. In one landmark case, OpenAI’s ChatGPT was fined for violations of data privacy regulations,
highlighting the growing legal exposure businesses face when deploying AI. Governments in the Middle East are still adapting to these rapid
changes, often leaving businesses in a regulatory grey zone.

Major AI cybersecurity risks

As artificial intelligence becomes increasingly integrated into business operations, it also introduces complex cybersecurity risks. Gartner
forecasts a period of ‘AI turbulence’ by 2025, as attackers shift from traditional infrastructure to the more vulnerable components of AI
systems, such as data pipelines, machine learning models and AI agents.

One of the most concerning threats is data poisoning, where attackers manipulate the training data used to build AI models. Even minor
alterations can distort outputs, leading to reputational damage, financial losses or even system-wide failures. In addition, AI models are at risk
of being reverse engineered through model extraction or inversion attacks. These tactics enable cybercriminals to steal proprietary algorithms
or infer sensitive training data, putting intellectual property and data privacy at significant risk.

Generative AI systems face another layer of vulnerability through prompt injection attacks with malicious inputs manipulating system
behaviour and integrity. Meanwhile, model evasion techniques allow adversaries to bypass detection, rendering security models useless.

Cybersecurity framework for organisational resilience
Securing artificial intelligence is not a one-time task but a continuous, end-to-end process that demands a comprehensive approach. For
instance, at Paramount, we address this through a cybersecurity framework built on four essential pillars.

The first pillar is governance and policy, which provides the foundational guardrails for safe AI usage. This involves developing clear,
enforceable policies covering ethical AI use, data privacy, third-party integration, and data minimisation.

The second pillar is secure AI lifecycle management, where security is integrated at every stage, from data collection and model training to
deployment and decommissioning. For example, rigorous validation should be conducted during training to detect poisoned datasets and
real-time monitoring to identify anomalies indicative of adversarial inputs. Like an aircraft requiring both pre-flight checks and in-air
maintenance, AI systems must be scrutinised throughout their lifecycle.

The third pillar positions AI as a cybersecurity ally. AI-driven security tools can analyse vast datasets, detect anomalies, and automate
responses with exceptional speed and precision. In areas like Identity and Access Management (IAM), AI can dynamically adjust access
privileges based on risk signals, providing critical protection in modern, hybrid IT environments.

Finally, data and integration security ensures the integrity and confidentiality of data shared across systems. With the rise of cloud-first and
multi-cloud environments in the Middle East, practices such as encryption, zero-trust architecture, secure APIs and integration firewalls are
crucial to protecting sensitive information from misuse.

By aligning with industry frameworks like AI TRiSM, organisations can go beyond technical protection to build trust and operational
confidence. Ultimately, cybersecurity should be seen as a strategic enabler of secure, scalable AI adoption.

Way forward: emerging trends and transformative potential of AI
Looking ahead, the future of AI security is being shaped by several emerging trends. These include AI Red Teaming, which simulates
adversarial attacks to detect weaknesses in AI models, and Quantum-Resistant AI Encryption, which prepares systems for the post-quantum
era, where current encryption may no longer be viable. Additionally, Compliance-as-a-Service (CaaS) platforms are becoming essential as
regulatory frameworks such as the EU AI Act and upcoming standards in the GCC region make compliance a core business requirement.

However, effectively harnessing these advancements requires a shift in approach, prioritising requirement-first thinking. Rather than
beginning with tools, organisations must first define the business outcomes they seek, the risks they can tolerate, and the data they need to
safeguard. In regions like the Middle East, where initiatives such as Saudi Arabia’s Vision 2030 and the UAE’s digital economy strategy rely
heavily on technology, maintaining trust through strong cybersecurity measures is vital for AI to truly drive progress.

AI holds immense potential to transform businesses across sectors – from predictive maintenance in oil rigs to smart city planning and hyper-
personalised banking – but its power also poses significant risks if misused. For organisations, especially in the Middle East, the path forward
lies in balancing innovation with robust cybersecurity. Security should be seen as the foundation for ethical, sustainable AI adoption. As we
advance into the digital era, deploying secure AI systems will be key to responsibly leading in innovation.

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