Securing AI infrastructure is critical – here's how to do it | Tech Radar

Overview

Securing AI infrastructure is critical – here's how to do it

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.

Details

I believe that 2026 will be a defining year for cybersecurity.

Sometime during the year, AI-powered threats will have the ability to adapt in real time. This will force organizations to defend against them – and they will need to do it fast.

Some of the AI-enabled cyberattacks will be against AI systems, which are already becoming deeply embedded across business operations – from decision-making and automation to customer engagement and critical services.

Friend or foe? AI: The new cybersecurity threat and solutions

The Human Risk Reckoning: Why security must evolve for an AI-augmented workforce

The power and potential of agentic AI in cybersecurity

Chief Information Security Officer at Quorum Cyber.

Modern AI infrastructure spans models, training frameworks, data pipelines, RAG architectures, APIs, open-source libraries, development tools, and deployment environments. While large-scale breaches of AI infrastructure have not yet become mainstream, the threat landscape is evolving fast – and the potential impact is severe.

The question is no longer if AI infrastructure will be targeted, but how prepared organizations are when it is.

Before looking at threats, it’s important to understand that AI infrastructure comprises these components:

Each of these components represents a potential attack surface – and none exist in isolation.

While AI breaches remain relatively rare today, several realistic and increasingly observed threat scenarios are emerging:

Data poisoning at scale: Attackers manipulate pre-training, fine-tuning, or embedding data to introduce hidden vulnerabilities, biases, or backdoors. These issues may remain dormant until triggered, compromising model integrity and trustworthiness.

Model supply chain compromise: Backdoored foundation models or dependencies are distributed through legitimate channels, exposing organizations that unknowingly integrate them into production systems.

Adversarial attacks: Real-time manipulation of model inputs causes misclassification or incorrect outputs – a serious risk when AI is used in security, finance, or safety-critical environments.

When things go wrong: Catastrophic AI threat scenarios

The real concern lies in how these threats scale. Here are a few serious scenarios:

Critical infrastructure manipulation: Compromised AI systems controlling power grids, transportation networks, or healthcare environments could make unsafe or malicious decisions.

Widespread misinformation: Poisoned models deployed across multiple organizations could be used to generate consistent, large-scale misinformation, eroding trust and amplifying harm.

Intellectual property theft: Model extraction attacks may expose proprietary algorithms, training data, or sensitive business logic, resulting in long-term competitive and financial damage.

These scenarios underline one key truth: AI infrastructure must be treated as mission critical.

Friend or foe? AI: The new cybersecurity threat and solutions

The Human Risk Reckoning: Why security must evolve for an AI-augmented workforce

The power and potential of agentic AI in cybersecurity

AI environments introduce new risks that traditional security models weren’t designed to handle. Increases in adversarial attacks, supply chain compromises, and AI-specific zero-day exploits mean reactive security approaches are no longer sufficient.

Securing AI infrastructure requires a defense-in-depth mindset, applied across every layer of the AI lifecycle.

The key is treating AI infrastructure as a critical, interconnected system requiring defense-in-depth strategies.

With increases in adversarial attacks corrupting AI training data, supply chain attacks targeting AI model updates, and zero-day exploits designed to compromise AI security systems making proactive security measures essential rather than optional.

Implement model provenance tracking and digital signing

Use differential privacy during training to prevent data leakage

Deploy adversarial robustness testing and red-teaming exercises

Establish secure model registries with access controls

Monitor for model drift and unexpected behavior patterns

Implement data lineage tracking and validation at ingestion

Use privacy-preserving techniques like federated learning

Establish data sanitization and anomaly detection pipelines

Implement secure data governance with classification and retention policies

Deploy continuous monitoring for data quality and integrity

Secure vector databases with encryption and access controls

Implement input validation and sanitization for queries

Use context-aware filtering to prevent information leakage

Deploy retrieval monitoring to detect unusual access patterns

Establish secure knowledge base management with version control

Implement comprehensive dependency scanning and vulnerability management

Use software bill of materials (SBOM) tracking for all AI components

Establish secure development practices with code signing

Maintain an approved library registry with regular security assessments

Implement zero-trust network architecture for AI workloads

Use hardware security modules (HSMs) for model encryption

Deploy comprehensive logging and monitoring across all AI systems

Establish incident response procedures specific to AI threats

Implement regular security assessments and penetration testing

Establish AI governance frameworks with clear accountability

Implement human-in-the-loop validation for critical decisions

Deploy model behavior monitoring and alerting systems

Maintain disaster recovery and business continuity plans

Regular security awareness training focused on AI-specific threat

AI is becoming a powerful force multiplier for organizations and threat actors alike. As the technology matures, so too will the methods used to exploit it.

Treating AI infrastructure as a critical, interconnected system – and securing it accordingly – is no longer optional. The organizations that act early will be best positioned to benefit from AI without exposing themselves to unnecessary and potentially catastrophic risk.

We've featured the best endpoint protection software.

This article was produced as part of Tech Radar Pro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of Tech Radar Pro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

Chief Information Security Officer at Quorum Cyber.

You must confirm your public display name before commenting

1A worrying Wi-Fi vulnerability can bypass home and office network encryption - here's how to stay safe

2I’m a Pokémon mega fan, and I’ve found the best gifts you can find to celebrate Pokémon Day 2026 — the franchise’s 30th anniversary

3 Quordle hints and answers for Saturday, February 28 (game #1496)

4NYT Connections hints and answers for Saturday, February 28 (game #993)

5NYT Strands hints and answers for Saturday, February 28 (game #727)

Tech Radar is part of Future US Inc, an international media group and leading digital publisher. Visit our corporate site.

Key Takeaways

Securing AI infrastructure is critical – here's how to do it
When you purchase through links on our site, we may earn an affiliate commission
I believe that 2026 will be a defining year for cybersecurity
Sometime during the year, AI-powered threats will have the ability to adapt in real time
Some of the AI-enabled cyberattacks will be against AI systems, which are already becoming deeply embedded across business operations – from decision-making and automation to customer engagement and critical services