The surge in AI adoption across industries is reshaping how organisations operate. In a race to stay competitive, businesses are turning to AI to streamline operations, enhance customer experiences, identify potential risks, and increase overall organisational efficiency. Global studies show that 82% of organisations are already using or exploring AI, with the AI market expected to reach $1.85 trillion by 2030.
While AI holds great promise as a force for good, its adoption brings introduces a host of potential complexities. The pressure to innovate, means that critical vulnerabilities are often overlooked, leaving organisations severely exposed. In fact, 87% of security professionals report that their organisation faced an AI-driven cyberattack last year, underscoring the urgent need for tailored security measures.
To fully harness the benefits of AI, organisations must ensure they have complete visibility and control over the infrastructure that supports it – be that on-prem or cloud systems hosting the models or the data that powers them.
Organisations must remember that AI is data, and data is AI. The effectiveness of AI models depends on the data they consume and the data sets they access. For this reason, organisations must have network-level visibility of all data in motion, especially that which heavily interacts with the AI model. Ultimately, the question is no longer if organisations should secure their AI infrastructure, it’s how this can be done.
The Good and the Bad
Recent technological developments in AI have been a powerful aid for organisations. But those same capabilities have also helped attackers. Recent advances in AI have effectively lowered the barrier to entry for cybercrime. Threat actors no longer need technical expertise to launch sophisticated attacks, as with the necessary prompts, AI constructs and conducts the cyberattacks for them.
As organisations continue to leverage AI, attackers will too. Cybercriminals are rapidly scaling their efforts to outpace traditional defences, and this evolving threat landscape requires a fundamental shift in mindset. AI infrastructure is not static. It is continuously evolving based on the data it processes and generates. As a result, security strategies must evolve in parallel. To stay ahead, organisations must commit to ongoing oversight of their AI systems – understanding how AI models interact with their systems and data, and how those interactions can change over time. Without this level of insight, the same tools that enable innovation can just as easily become vectors for attack.
The Potential Consequences
With digital threats evolving at the same pace as advancements in AI, the risks of failing to properly understand and monitor AI systems are more serious than ever. If organisations lack the visibility necessary to understand where AI solutions are being used and be able to track the data fed into their AI infrastructure, they may inadvertently process or expose sensitive information, potentially leading to the violation of privacy laws like GDPR, CCPA or HIPAA. This, in turn, could lead to hefty fines, legal action, and loss of customer and stakeholder trust.
Beyond data exposure, a lack of transparency and data governance could lead to non-compliance with local and international data protection laws. As regulatory frameworks become more stringent and AI-specific laws emerge, organisations that cannot demonstrate accountability for their AI systems face heightened legal and reputational risks. In such an environment, compliance isn’t optional, it’s a prerequisite for innovation.
The Role of Deep Observability
Complete network-level visibility into the data feeding AI infrastructure is imperative for organisations to leverage AI securely. Whether encrypted or not, data in motion must be visible to security teams. This level of insight can often be difference between stopping a threat in its tracks and suffering a breach, and the only way that to achieve this is through deep observability obtained from network level intelligence.
Network derived intelligence enables organisations to clearly understand what data is being shared with AI models and what models are in use. This, in turn, allows organisations to track audit scores, data handling, and transformation, ensuring AI decisions are transparent, traceable, and accountable. Should anything go wrong, organisations can pinpoint the origin of the issue, whether it’s a dataset, processing step, or output. Additionally, when Network derived intelligence is shared with modern security tools, many of which leverage AI technology to identify and protect against attacks, it adds another powerful dimension to the existing data sets they consume such as metrics and log files. This in turn increases the effectiveness of an organisations defensive AI capabilities.
This level of network visibility granularity plays a crucial role in mitigating data privacy, security and compliance risks, protecting sensitive information and preventing security breaches or regulatory violations.
Cyberattacks are growing in frequency and sophistication, while the regulatory landscape is becoming increasingly complex. Complying with regulatory acts such as the Digital Operations Resilience Act or NIS 2 is already challenging, and this pressure will only increase as AI use expands. To remain both secure and compliant, deep observability and network level intelligence is no longer a nice-to-have, it’s a foundational element for proper AI adoption. As AI continues to change how organisations operate, the threats surrounding it will only grow more complex. A reactive approach is no longer sufficient. To move forward with confidence, organisations must take control of their risks by embedding visibility into every part of their AI deployments.
