The imperative to prepare for AI-safe data security

Date:
Fri, 30 Oct 2026 07:41:55 +0000

Description:
Helping security leaders take the right approach to securing their data in
the AI era.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter The proliferation of AI tools
in business is redefining the need for data security and trust. As
autonomous, decision-making AI agents move from pilot projects into core business workflows, they are expanding the enterprise attack surface just as fast as they are being adopted.

Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, a jump from less than 5% in 2025. Gartners advice to leaders of software organizations is to set their agentic AI strategy or risk being outpaced by the competition. Security is changing. What was once a simple focus on encryption and perimeter defense is no longer enough on its own: the rise of AI-driven risk has transformed it into a multi-dimensional challenge built on algorithmic trust, model integrity, and synthetic data exposure. Trust boundaries are shifting, and most companies arent yet equipped to handle it. Latest Videos From TechRadar Watch full
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EMEA CTO, Hitachi Vantara. Yet AI has already been proven unsafe by its very makers, with the Hugging Face breach showing it was a rogue AI agent's fault. The unknown future risk of autonomous agents capable of emergent behavior poses huge issues for maintaining security. But this is just one risk of many faced by enterprise cyber security leaders.

In fact, the statistics of cyber security investment highlight that business is vastly underprepared for new threats. While the estimated annual cost of cybercrime is $10.5 trillion globally and predicted to reach $15.63 trillion by 2029, this eclipses Gartners information security spend prediction of $244 billion in 2026. You may like Why agentic AI demands a new approach to enterprise security How to accelerate AI adoption without creating
unnecessary security risk IT and security leaders of the AI era must go back to basics and beware the hype

Machine data is exacerbating the data quality issue with garbage in, garbage out. This calls for technology and trust architectures to be redesigned, with internet security embedded into the data lifecycle. Security leaders will
need to make considered investments in AI-safe security, transparency, and federated governance to protect their enterprise ecosystems. The biggest security risks were underestimating right now With many threats now
AI-driven, its unlikely that a single enterprise has protection against all the necessary risks, which is why its incumbent on business leaders to prioritize next-level data security. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me
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Firstly, polymorphic, AI-generated attacks are hitting bigger and faster, beyond any companys human ability to counter these attacks. Deepfake is one such AI-powered theft and extortion technique that can be generated in minutes, costing a mere $1,000 (if that) to create a malicious bot that can hack into your system and go after your business crown jewel data.

Using these methods, hackers can affect AI models by manipulating decision-making itself instead of through direct attacks on the IT infrastructure . Shadow AI and exposure to synthetic data are also growing concerns as employees adopt unsanctioned or non-firewalled AI tools, models and workflows to complete mundane tasks and reporting.

Next is the surge in agentic adoption without control of the risk, especially third-party risk, which is huge. Agentic AI has significantly increased the threat surface, with systems capable of autonomously taking actions. In the rush to implement AI, access is being granted for agents to all kinds of
data, even to highly sensitive data, often bypassing traditional enterprise controls. All kinds of blind spots emerge when agents are connected to external systems whose security vulnerabilities arent vetted. What to read next The ascent of autonomous attacks and the race to contain them AI has crossed a cybersecurity redline now what? AI floods security teams with findings. The advantage is in what happens next

Prompt injections are another critical risk, on a similar level to the whole third-party dependency. It's the idea that an organization doesnt know what
is actually making its way into their system. At first appearing benign, it can build up to turn malicious at some point in time. When it hits, the business won't know what hit them.

As if this isnt enough, were counting down to Q-Day, when quantum computers become powerful enough to break todays widely used encryption software standards. This is the stage when attackers will be able to intercept, decrypt, and compromise nearly all global digital communications, financial transactions, and other forms of secure data. Key considerations for a responsible AI approach Whats needed is a formal framework for trust-enabled ROI in the age of AI. It needs to be about maintaining data source integrity. In order to trust the data, there are various mechanisms or things that an enterprise needs to take care of such as identity and accountability.

We must know who's acting on the data and have context and control. We must then authorize it contextually, not implicitly and this means constantly validating behavior and intent. This is all while staying compliant against
or changing the regulatory landscape and maintaining human oversight across the lifecycle.

Responsible AI requires an overall shaping of an approach that considers the impact on the entire value chain in the ecosystem. Only this ensures that the value is delivered to the people who are putting into the system or that are impacted by the system. The following are essential elements to move this forward: Accountable humans The concept of accountability is key. Theres a tendency to say if something bad happens, you blame the machine, you blame
the algorithm, you blame the system. There need to be accountable humans that take responsibility. AI is here for the long term, and will have generational impacts, so if we don't understand that and take accountability for it, we're going to make wrong decisions. Four dimensions of responsible AI At a high level, the focus should center around transparency, accountability, fairness, and protection. Some key questions should be asked as part of building a framework for successful outcomes, such as: How do you make sure that your AI models and data practices are transparent? How do you make sure you drive accountability for AI-driven decisions? How do you make sure you're fair to the people affected by your AI systems? How do you make sure that you provide protection or security across the entire data lifecycle? Building a trust premium Amidst this new risk landscape, trust has become a measurable competitive asset for enterprises. Organizations that are moving their cyber security strategy to the next level and investing early in AI governance, cryptographic modernization and security architecture are building a trust premium. PwC research backs this up: across thousands of CEOs surveyed globally for its 25th Annual Global CEO Survey, customer trust explained 31% of the variance in profit margins and 21% in return on assets - a stronger link to financial performance than company size.

Durable trust requires an approach that is built and maintained across the entire operational life-cycle of data. This full-lifecycle view of trust comprises data source integrity, end-to-end data quality, identity and accountability, behavioural monitoring and intent validation, regulatory compliance and effective governance, applying human oversight across the AI lifecycle. Taking a methodical approach to protection The changing frontiers of data security demand a fresh approach, which involves building resilient data infrastructures and best practices that can stop threats in their
tracks. Future successful enterprise security will depend as much on
governing autonomous systems as well as defending networks.

The right approach is to take a step back, think holistically, considering
all the building blocks that need to be put in place, and then be more methodical. A methodical approach protects the business, its customers, and
it protects a society so that we can all progress.

Doing things right in the right way will have multiple cascading benefits, including guaranteed value for customers and the longevity of the business, both of which links to trust and loyalty. We've Reviewed, Rated, and Ranked the Best Firewall Software. This article was produced as part of TechRadar
Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

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