In this article, we cover:
- Adoption has outpaced protection: 88% of organisations use AI for business needs, but lack correct security measures. Even with over 5.19 million cybercrimes occurring annually organisations still treat cybersecurity as an afterthought.
- Humans remain the biggest vulnerability: Humans are directly involved in 60% of cyber breaches, highlighting the need for strong employee awareness training.
- Governance is the foundation:. Clear frameworks defining approved tools, acceptable use, and data handling are essential to building resilient AI systems.
AI in business
Many organisations focus their AI strategies on improving efficiency and advancing innovation but ignore protecting the technology itself.
As AI continues to change how businesses operate, it adds new business value by automating many repetitive processes, managing large volumes of data, and making informed business decisions.
Many companies don’t think about the dangers of using AI without governance in place. According to Brijesh Patel, Senior Information Security Officer at FDM, “Keeping organisations safe from cyber threats should be a part of how we use AI from the very beginning, not something we add later.”
Approximately 88% of organisations use AI for some of their business needs; however, they don’t have the right security measures in place. Every year, there are over 5.19 million cybercrimes. This shows how important it is for companies to make cybersecurity a top priority in their AI strategy. They need to think of cybersecurity as the foundation of their AI plan, or they could be leaving themselves open to all sorts of risks.
What are common security risks in AI projects?
The average cost of an AI attack is $6 million, so understanding the risks AI can pose is the first step towards building resilient AI systems.
Data poisoning: Hackers deliberately try to feed corrupt data into LLM training. AI models rely on high-quality data, so if inaccurate information is shared with them during training, they can produce inaccurate results.
Third-party and supply chain risks: AI models can connect multiple systems like cloud platforms, APIs, and any third-party services. The more systems that are connected, the higher the points of vulnerability and the greater chances that an attack could happen.
Unauthorised access: Multi-factor authentication (MFA) and role-based access control (RBAC) systems are effective ways to allow only the right people to access sensitive data.
How can organisations prioritise AI security risks?
One of the most critical roles of a security team within an organisation is to discover the organisation’s weaknesses. They should then decide which risks need to be fixed right away and which can be monitored for a while.
A practical approach is to assess these areas:
Likelihood of exploitation: How easily could an attacker exploit our data? Do we have a system in place if someone attacked our systems?
Exposure: Is the AI application publicly accessible, customer-facing or limited to internal use? Taking a risk-based approach enables organisations to focus resources where they will have the greatest impact.
When it comes to prioritising security issues, Brijesh shares, “It’s crucial to consider how sensitive the data is and whether the existing security measures are reducing the risk.”
Teams still should evaluate each issue in the context of the business’s priorities.
How can organisations protect AI systems?
AI-powered tools like Senseon can help flag anomalies, but human awareness is still your strongest defence.
When employees use AI tools that are available to the public without getting permission from their company, it’s called shadow AI. Studies have found that over 90% of employees use their own chatbot accounts to do their daily tasks, and often they do this without the IT department even knowing about it.
Company data shared with unauthorised platforms can be stolen or misused without the company even realising, which is a major concern for organisations.
Over-reliance on AI-generated outputs is another problem for organisations. LLMs (large language models) produce convincing but inaccurate responses, meaning every output needs to be seen by humans, especially when used for high-impact decisions.
Brijesh shares, “Organisations should understand how data is managed, what security standards are followed and whether customer information is used to train future AI models. Vendor due diligence with third-party AI platforms is also important for AI providers.”
Establishing clear AI governance frameworks that define approved tools, acceptable use policies, data handling requirements and ongoing security reviews to minimise these risks is strongly encouraged.
Why is AI governance important?
Humans cause the majority of security incidents, whether through deliberate phishing attacks or the accidental sharing of sensitive information.
Effective AI security awareness programmes can help employees understand:
- What information shouldn’t be entered into AI tools
- How to recognise suspicious AI behaviour
- The importance of verifying AI-generated content
- Secure handling of customer, financial and commercially sensitive information.
- How and when to report potential AI-related security incidents
Teaching employees about AI can help them use AI tools correctly and understand potential biases.
What are the best AI security practices for building secure AI systems?
Monitoring and continuous improvements are key for securing AI systems, although there are other factors to consider too:
Data protection
DLP (Data Loss Prevention) technologies can help monitor how data moves and flag and block attempts to transfer sensitive information without permission.
Katrina Gallagher, Head of Privacy at FDM, says, “The main problem with keeping data safe is that companies don’t have rules in place to follow.”
Employee awareness
Cyber breaches often happen because of phishing emails and when someone’s credentials are compromised. Studies show that in 60% of breaches, a person was directly involved. If employees are given clear guidelines on the safe use of AI tools and made aware of their risks, you can lower the chances of these incidents.
Incident response plan
Organisations should develop and implement an incident response plan to address compliance violations or breaches, promptly. Common strategies include isolating infected systems, investigating the attack, protecting client information, and restoring systems to normal operations in a timely fashion if the application is compromised.
How organisations can build secure AI capabilities with FDM
Organisations need the right people, governance and technical expertise to design, deploy and manage AI solutions responsibly. At FDM, we assist companies in developing the skills they need to use AI in a secure and large-scale manner. Our team of experts, including AI consultants, cybersecurity specialists, data engineers, and governance professionals, work closely with clients to enhance security, implement best practices, and build the skills necessary to navigate the constantly changing threats they face.
Conclusion
Each AI system brings its own set of security risks. Without a robust cyber strategy in place, AI-enabled fraud is projected to cost organisations 40 billion USD by 2027; organisations must implement one and equip their workforce to respond to threats.
Explore how FDM can help futureproof your cyber workforce.