Artificial intelligence is no longer just powering defensive cybersecurity tools, it is reshaping the entire threat landscape. AI is accelerating reconnaissance, improving the realism of phishing, automating malware mutation, and enabling adaptive attack techniques. At the same time, enterprises are embedding AI agents, copilots, and generative AI tools into everyday workflows.

That dual dynamic has created a new category: AI security.

AI security platforms focus on three primary challenges in 2026:

1. Securing enterprise AI usage and prompt interactions
2. Protecting AI models, agents, and infrastructure
3. Defending against AI-powered cyber threats

Below are five of the strongest AI security solutions in 2026.

Check Point – AI-driven security

Check Point integrates AI security into its broader Infinity platform, covering network, cloud, endpoint, and AI usage in a unified architecture.

The core of the platform is ThreatCloud AI, which leverages more than 50 AI engines and intelligence from over 150,000 connected networks. Compromise indicators propagate across the platform within seconds, enabling coordinated defense across domains.

The platform addresses AI risk at multiple layers. GenAI Protect monitors employee interactions with generative AI tools, semantically analysing prompts to enforce data loss prevention policies in real time. This approach focuses on contextual classification rather than simple keyword matching.

Check Point also secures AI infrastructure and enhances security operations through Infinity AI Copilot. Independent testing has shown high efficacy against zero-day malware, and the platform has consistently ranked highly in hybrid firewall evaluations.

Best for: Enterprises seeking unified AI security across infrastructure, AI usage, and security operations.

CrowdStrike – AI security services

CrowdStrike extends its Falcon platform into AI protection by integrating telemetry from endpoints, identities, cloud workloads, and AI agent activity.

Falcon AIDR focuses specifically on defending against prompt injection and malicious manipulation of AI agents. It is designed to identify known prompt injection techniques while maintaining low latency, which is critical in production AI environments.

CrowdStrike also integrates AI assistants directly into security operations. Charlotte AI supports natural language threat investigation and automated triage, reinforcing the company’s vision of an AI-augmented SOC.

The approach is particularly strong for organisations already standardised on the Falcon ecosystem, allowing AI security capabilities to extend existing endpoint and cloud telemetry.

Best for: Organisations seeking integrated AI threat detection within an established endpoint-centric security architecture.

Cisco – AI defense

Cisco approaches AI security from a network-centric vantage point. Because it operates at the network layer, Cisco can inspect AI-related traffic across enterprise environments, including API calls and model interactions that may not be visible at the endpoint level.

Cisco AI Defense integrates into the broader Security Service Edge architecture. Recent enhancements include AI Bills of Materials to map dependencies within AI ecosystems, real-time guardrails for agentic systems, and red teaming simulations against AI workflows.

Cisco aligns its controls with established frameworks such as NIST AI Risk Management Framework and MITRE ATLAS. This emphasis on governance makes it attractive to enterprises operating in regulated industries.

Best for: Enterprises with strong Cisco network infrastructure seeking AI security embedded at the traffic and control layer.

Microsoft– AI-enhanced security ecosystem

Microsoft’s AI security advantage lies in scale. The company processes tens of trillions of security signals daily across its global infrastructure.

Security Copilot functions as an AI assistant embedded within Defender, Entra, Intune, and Purview. It automates alert triage, assists with natural language threat investigation, and orchestrates remediation actions.

Microsoft has also expanded AI security posture management to include multi-cloud environments, including AWS and Google Cloud AI services. This is particularly important for enterprises building AI models outside Azure.

For organisations already invested in Microsoft 365 enterprise licensing, AI-enhanced security capabilities can be layered into existing subscriptions without introducing additional vendor complexity.

Best for: Enterprises deeply aligned with Microsoft 365 and Defender ecosystems.

Okta– Identity security with AI risk context 

As AI agents proliferate, identity becomes a primary attack surface. Many AI systems operate with high levels of privilege and autonomy.

Okta focuses specifically on identity governance in AI environments. Its architecture treats AI agents as first-class identities, applying authentication, authorisation, and lifecycle governance controls similar to those applied to human users.

Identity Security Posture Management identifies over-privileged accounts, including non-human identities, and surfaces risk in real time. The company also promotes open standards for managing AI-to-application connectivity through extended OAuth mechanisms.

For enterprises rapidly deploying AI agents internally, identity-centric AI security becomes essential.

Best for: Organisations deploying AI agents at scale that require identity governance for non-human actors.

Comparison Overview

How to choose the right AI security solution

Selecting the right AI security platform depends on architecture and maturity.

Organisations building AI internally should prioritise infrastructure protection and identity governance. Enterprises concerned with employee generative AI usage should evaluate prompt monitoring and DLP integration. Security teams overwhelmed by alert volume may prioritise AI-augmented SOC automation.

AI security is not a separate silo. It intersects with network security, identity management, cloud governance, and incident response.

The platforms above represent different strategic entry points into AI risk management. The best solution is the one aligned with your existing ecosystem and operational model.

In 2026, AI is both a tool and a target. Enterprises that treat AI security as an integrated part of their security architecture will be better positioned to manage evolving threats.

Image source: Pixabay

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Among the big players in technology, Cisco is one of the sector’s leaders that’s advancing operational deployments of AI internally to its own operations, and the tools it sells to its customers around the world. As a large company, its activities encompass many areas of the typical IT stack, including infrastructure, services, security, and the design of entire enterprise-scale networks.

Cisco’s internal teams use a blend of machine learning and agentic AI to help them improve their own service delivery and personalise user experiences for its customers. It’s built a shared AI fabric built on patterns of compute and networking that are the product of years spent checking and validating its systems – battle-hardened solutions it then has the confidence to offer to customers. The infrastructure in play relies on high-performance GPUs, of course, but it’s not just raw horse-power. The detail is in the careful integration between compute and network stacks used in model training and the quite different demands from the ongoing load of inference.

Having made its name as the de facto supplier of networking infrastructure for the enterprise, it comes as no shock that it’s in network automation that some of its better-known uses of AI finds their place. Automated configuration workflows and identity management combine into access solutions that are focused on rapid network deployments generated by natural language.

For organisations looking to develop into the next generation of AI users, Cisco has been rolling out hardware and orchestration tools that are aimed explicitly to support AI workloads. A recent collaboration with chip giant NVIDIA led to the emergence of a new line of switches and the Nexus Hyperfabric line of AI network controllers. These aim to simplify the deployment of the complex clusters needed for top-end, high-performance artificial intelligence clusters.

Cisco’s Secure AI Factory framework with partners like NVIDIA and Run:ai is aimed at production-grade AI pipelines. It uses distributed orchestration, GPU utilisation governance, Kubernetes microservice optimisation, and storage, under the umbrella product description Intersight. For more local deployments, Cisco Unified Edge brings all the necessary elements – compute, networking, security, and storage – close to where data gets generated and processed.

In environments where latency metrics are critically important, AI processing at the edge is the answer. But Cisco’s approach is not necessarily to offer dedicated IIoT-specific solutions. Instead, it tries to extend the operational models typically found in a data centre and applies the same technology (if not the same exact methodology) to edge sites. It’s like data centre-grade security policies and configurations available to remote installations. Having the same precepts and standards in cloud and edge mean that Cisco accredited engineers can manage and maintain data centres or small edge deployments using the same skills, accreditation, knowledge, and experience.

Security and risk management figure prominently in the Cisco AI narrative. Its Integrated AI Security and Safety Framework applies high standards of safety and security throughout the life-cycle of AI systems. It considers adversarial threats, supply chain weakness, the risk profiles of multi-agent interactions, and multi-modal vulnerabilities as issues that have to be addressed regardless of the nature or size of any deployment.

Cisco’s work on operational AI also reflects broader ecosystem conversations. The company markets products for organisations wanting to make the transition from generative to agentic AI, where autonomous software agents carry out operational tasks. In most cases, this requires new tooling and new operational protocols.

Cisco’s future AI plans include continuing its central work in infrastructure provision for AI workloads. It’s also pursuing broader adoption of AI-ready networks, including next-gen wireless and unified management systems that will control systems across campus, branch, and cloud environments. The company is also expanding its software and platform investments, including its most recent acquisition (NeuralFabric), to help it build a more comprehensive software stack and product portfolio.

In summary, Cisco’s AI deployment strategy combines hardware, software, and service elements that embed AI into operations, giving organisations a route to production-grade systems. Its work can be found in large-scale infrastructure, systems for unified management, risk mitigation, and anywhere that connects distributed, cloud, and edge computing.

(Image source: Pixabay)

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A few years ago, the business technology world’s favourite buzzword was ‘Big Data’ – a reference to organisations’ mass collection of information that could be used to suggest previously unexplored ways of operating, and float ideas about what strategies they may best pursue.

What’s becoming increasingly apparent is that the problems companies faced in using Big Data to their advantage still remain, and it’s a new technology – AI – that’s making those problems rise once again to the surface. Without tackling the problems that beset Big Data, AI implementations will continue to fail.

So what are the issues stopping AI deliver on its promises?

The vast majority of problems stem from the data resources themselves. To understand the issue, consider the following sources of information used in a very average working day.

In a small-to-medium sized business:

• Spreadsheets, stored on users’ laptops, in Google Sheets, Office 365 cloud.
• The customer relationship manager (CRM) platform.
• Email exchanges between colleagues, customers, suppliers.
• Word documents, PDFs, web forms.
• Messaging apps.

In an enterprise business:

• All of the above, plus,
• Enterprise resource planning (ERP) systems.
• Real-time data feeds.
• Data lakes.
• Disparate databases behind multiple point-products.

It’s worth noting that the simple list above isn’t comprehensive, and nor is it intended to be. What it demonstrates is that in just five lines, there are around a dozen places where information can be found. What Big Data needed (perhaps still needs) and what AI projects also rest on, is somehow bringing all those elements together in such a way that a computer algorithm can make sense of it.

Marketing behemoth Gartner’s hype cycle for artificial intelligence, 2024, placed AI-Ready Data on the upward curve of the hype cycle, estimating it would be 2-5 years before it reached the ‘plateau of productivity’. Given that AI systems mine and extract data, most organisations – save those of the very largest size – don’t have the foundations on which to build, and may not have AI assistance in the endeavour for another 1-4 years.

The underlying problem for AI implementation is the same as dogged Big Data innovations as they, in the past, made their way through the hype cycle – from innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, to plateau of productivity – data comes in many forms; it can be inconsistent; perhaps it adheres to different standards; it may be inaccurate or biased; it could be highly sensitive information, or old and therefore irrelevant.

Transforming data so it’s AI-ready remains a process that’s as relevant today (perhaps more so) than it’s ever been. Those companies wanting to get a jump start could experiment with the many data treatment platforms currently available, and as is becoming the common advice, might begin with discrete projects as test-beds to assess the effectiveness of emerging technologies.

The advantage of the latest data preparation and assembly systems is that they are designed to prepare an organisation’s information resources in ways that are designed for the data to be used by AI value-creation platforms. They can offer, for example, carefully-coded guardrails that will help ensure data compliance, and protect users from accessing biased or commercially-sensitive information.

But the challenge of producing coherent, safe, and well-formulated data resources remains an ongoing issue. As organisations gain more data in their everyday operations, compiling up-to-date data resources on which to draw is a constant process. Where big data could be considered a static asset, data for AI ingestion has to be prepared and treated in as close to real-time as possible.

The situation therefore remains a three-way balance between opportunity, risk, and cost. Never before has the choice of vendor or platform been so crucial to the modern business.

(Source: “Inside the business school” by Darien and Neil is licensed under CC BY-NC 2.0.)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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