Building A Future-Ready GenAI Security Strategy

Uploaded on 2025-07-01 in TECHNOLOGY-Key Areas-Artificial Intelligence, FREE TO VIEW

Generative AI (GenAI) isn’t just another emerging technology, it’s a turning point for business automation, decision-making, and operational efficiency. But as adoption accelerates, so does risk.

Across the board, we’re seeing varying levels of preparedness from organizations when it comes to GenAI adoption. While some are readily embracing it, others remain cautious.

What’s clear is that there’s still a way to go for organizations to get the most out of GenAI. Over the next three years, 92% of companies plan to increase their AI investments, according to McKinsey. But while nearly all companies are investing in AI, only 1% of leaders call their companies “mature” on the deployment spectrum.

While data privacy, bias, compliance, and misuse are all valid concerns, letting these challenges outweigh the benefits isn’t a reason to delay adoption.

The path forward lies in building secure GenAI strategies that embed trust, control, and accountability into innovation.

GenAI Deployment: Balancing Risk With Innovation

With platforms like ChatGPT, employees began using large language models (LLMs) almost overnight. As GenAI models become faster and more sophisticated, adoption will only accelerate. While this brings massive productivity gains, it also introduces new vulnerabilities.

Business leaders must ask: How will we secure GenAI tools? How can we control what’s shared and where it goes? Are we truly ready to embrace GenAI?

Before deployment, organizations must consider key risks including:

Shadow AI. GenAI tools are rapidly entering workplaces, often without IT oversight. This is known as shadow AI and can lead to data breaches and compliance violations.

Supply Chain Vulnerabilities. The ecosystem of AI models, datasets, and third-party services creates new attack surfaces. Organizations must evaluate model provenance, assess vendor security practices, and ensure data integrity to avoid compromised or biased outputs.

Insider Threats. Insider threats are not new, but GenAI expands their reach. Whether it’s a well-intentioned employee inputting sensitive data into a public chatbot, or a malicious actor exploiting model vulnerabilities, the result is the same: accelerated risk. This drives the need for stronger access controls, oversight, and logging.

Advanced Attack Vectors. GenAI faces threats like prompt injection (manipulating model behavior), data poisoning (tampering with training data), and model extraction (stealing proprietary capabilities). These require specialized defenses like input validation, output filtering, and robust monitoring.

Model Hallucinations. GenAI may generate false, misleading, or fabricated outputs. While sometimes benign or creatively useful, others can cause reputational or operational damage. AI outputs should be treated as suggestions, not facts.

The Road To A Future-Ready GenAI Security Strategy

To deploy GenAI responsibly, security can’t be an afterthought. It must be a strategic pillar integrated from the earliest phases of AI adoption.

Organizations can quickly gauge their GenAI security readiness by asking three fundamental questions: Can we see it? Can we control it? Can we respond to it?

If employees are using AI tools without visibility into what data is being shared, you're operating reactively with significant blind spots. If you have basic policies and approved tool lists but lack real-time monitoring of AI interactions, you're managing risk but not optimizing for it.

The goal isn't perfection - it's progression from reactive shadow AI management to proactive, integrated security that scales with innovation.

This is made simple with the support of an expert cybersecurity consultancy that removes complexity from developing a comprehensive, risk-based strategy.

Organizations gain the competitive advantage through:

1. Assessing Risk Environments. Security basics remain essential, and a full risk assessment should precede GenAI deployment. This includes mapping data flows, evaluating third-party components, and understanding the evolving regulatory requirements around AI use—from the EU AI Act's high-risk system classifications to sector-specific compliance frameworks in healthcare, finance, and government. Organizations must also prepare for emerging executive orders and state-level AI regulations that may impact deployment strategies.

2. Maintaining Good Cyber Hygiene. Security is as much about behavior as it is about technology. Organizations should train users to understand GenAI functionality, recognize hallucinations, and avoid sharing sensitive data. They need to enforce clear usage policies, limit data exposure, and tighten access controls to reduce risk.

3. Prioritizing Detection and Response. GenAI security requires continuous oversight, not one-time implementation. Real-time monitoring, logging, and feedback loops are essential. Adversarial testing helps identify vulnerabilities before they escalate. When combined with human oversight, detection and response strategies can reduce risk and build trust into GenAI operations.

A Secure GenAI Strategy: From Awareness To Execution

AI is set to transform business processes as we know them. Preparing for this requires future-ready GenAI security strategies that lean into best practices. This is key to decreasing vulnerabilities, increasing threat awareness, and enabling sustainable processes.

As GenAI continues to evolve, accountability from business leaders is crucial for ensuring secure processes that are resilient to risk and flexible enough to scale.

Matthew Martin is CEO & Founder of Two Candlesticks

Image: Steve Johnson

You Might Also Read:

Insurers Are Now Covering AI Malfunction Losses:

If you like this website and use the comprehensive 8,000-plus service supplier Directory, you can get unrestricted access, including the exclusive in-depth Directors Report series, by signing up for a Premium Subscription.