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How leveraging Cyber AI technology could provide efficiencies in your security organisation

In the dynamic landscape of modern business, optimising artificial intelligence (AI) has never been more crucial. With the capacity to reshape and provide hidden efficiencies across numerous sectors, security organisations are turning to AI tools to enhance their security efforts. Erica Truong, Consultant at Crossword explains how leveraging cyber AI could open to door to business efficiencies.

Leveraging these cyber AI tools presents an unprecedented opportunity to revolutionise business efficiencies, provide insights into workflow optimisation while offering significant cost savings. However, there are inherent risks to consider with the scope of AI capabilities ranging from concerns around its significance and impact on the organisation, sustainability practices and the potential challenges on technology and wider society.

With the evolving threat landscape and proliferation of devices, many security organisations are adopting AI built technologies to enhance decision-making, improve security posture and manage aspects of data volume. The surge in generative AI, such as LLMs, has been recognised as a valuable resource that can not only help empower organisations to implement security controls but also provide an informative monitoring strategy tailored to address your specific needs. But how could generative AI help bridge the security gaps within your organisation?

Several benefits of current AI applications can be seen to help with:

Enterprise and cyber AI risk management

Cyber teams are now able to leverage AI tools that can independently gather data from enterprise information systems and monitor threat exposure. Cyber AI tooling can automate threat detection and minimise the attack surface by forecasting and detecting attacks in real time, which can in turn simplify incident response processes, reinforce best practices, and even increase GRC capabilities. This remarkable capacity allows vast amounts of data to be processed and patterns to be assessed that might be missed by the human eye. By reducing the risk of manual errors, it can help bolster operational resilience and enhance controls compliance.

Predictive capabilities and third-party risk management

AI powered systems can streamline third-party risk management by analysing vast data sets and monitoring the external factors that could impact your supply chain. This proactive approach can predict how and where you are likely to be breached and identify potential risks before manifesting into operational and financial vulnerabilities. With AI algorithms paired with machine learning (ML) predictive analysis, it reduces the excessive labour costs and offers a more streamlined risk monitoring solution.

Automate regulatory compliance

The use of AI can assist with routine compliance monitoring by continuously scanning for regulatory changes, analysing textual data and optimising service delivery. Cutting-edge tools such as ChatGPT and other LLMs are impressive in executing repetitive tasks, such as extracting relevant information to provide real-time insights, which enables work translated to become much more accessible. By anticipating potential risks along with the provision of early warning signs allows corrective actions to take place. Methods of incorporating deep learning can ensure policies are being achieved in line with regulatory requirements and resources to be allocated efficiently.

Policy management and internal audits

Beyond reducing the likelihood of non-compliance and using AI-driven data analysis to simplify the internal audit process, cyber AI is ushering in a new momentum with the ability to aid in streamlining policy creations. By leveraging AI capabilities, it can assist with review and ensure policies are kept updated, relevant and responsive to the changing landscape.

A Cyber AI Tooling strategy can also help equip you with a thorough understanding of your IT asset inventory, and other general applications that have access to your information systems. These capabilities as such can help align with the latest regulatory enforcement, and provide an insight to baseline categorisation, including a measurement of your business criticality. On a GRC front, many security organisations can take advantage of these AI tools where organisations regularly deal with heavy text and prescriptive language. The impact of these tools can effectively ease translation and help in understanding security gaps and vulnerabilities, but also analyse where the strength lies within your infosec program, to maintain a strong security posture.

Across various stages of policy lifecycles including policy formation, AI can be a powerful catalyst in ensuring policies are consistently followed within your organisation.

As we dig deeper into the area of AI, it is critical to recognise that the security issues related with AI and LLMs are more than simply theoretical concerns. They have real-world ramifications for both individuals and businesses. There are concerns about the potential misuse of AI technologies including larger risks, which range from data privacy breaches to the spread of misinformation, which could have massive implications if not effectively controlled.

Data Privacy Concerns and Ethical Considerations

As AI models require and process large volumes of data, there are often concerns around security and data privacy. The reality is that without these volumes of data, there is a risk that AI systems could produce false positive readings and deliver inaccurate results. In many cases, such models are so large that often it is not feasible for organisations to run them locally.

There are potential data privacy implications whereby the material output is sent to a provider. For example, if a cyber model has been trained on sensitive information, aspects of this data could be unintentionally exposed. Despite mitigation strategies to ensure secure data transmission and established agreements with external LLM providers, AI technologies could open pathways to override individual autonomy. To address these concerns, policies must be clear and rigorous to regulate the storage and collection of personal data by AI tools. Given the complexity of these models, it can be challenging to audit and define clear data ownership.

Bias and Discriminatory Outputs

A key challenge for generative AI and LLMs is down to delivering biased and discriminating outcomes, which is due to underlying biases in the data that is used to train the model. If a model has been trained on data containing discriminatory stereotypes or terms, its output could reinforce these biases. For example, LLMs that are trained on biased and skewed data deployed to customer services chatbots could produce biased and inaccurate responses, causing reputational and financial harm, and customer dissatisfaction.

Therefore, it is crucial to employ a diverse range of robust and unbiased data when training LLMs. Implementation of ethical guidelines for AI use can address LLMs to be used fairly and responsibly.

Lack of Transparency and Accountability

The internal processes of AI are frequently obscure, which results in an absence of transparency. This is a significant challenge for cyber security teams, since it is often difficult to comprehend the reason behind the LLM producing a specific output, undermining its credibility when used to aid decision-making processes. For example, if your organisation is dependent on generating content, it might be difficult to comprehend the model producing erroneous content without an in-depth understanding of its inner workings. To mitigate this, regular audits and assessments could help monitor the AI model’s outputs to ensure that they meet the expectations of your organisation.

Hallucinations and Liability

The predictive elements of AI models could factually provide an incorrect response which misaligns with its ML trained input. Software developers may rely on AI for recommendations on optimising code scripts or employ packages to import into their code for improvement. It is possible that these packages recommended by these models may not have been fully published, highlighting AI hallucinations.

The issue here is that AI models can make things up and it is unclear whether the output is true. The training model behind it could be using unstructured text data and other data sets based on biased human feedback. Additionally, it is difficult to solve and track back the IP element of content creation - for example, AI generated information could be taken from copyrighted content and transformed into model weights resulting in unclear material ownership and data input validation.

Additionally, adversarial attacks could pose significant risks to LLMs. Malicious attackers and ideological hackers could manipulate inputs into the model to generate vulnerabilities, employing the same AI techniques to break through defences and avoid detection, leading to the spread of misleading information and security breaches. As AI matures and moves increasingly rapidly in the cybersecurity space, organisations can limit this risk by implementing robust measures such as anomaly monitoring and input validation.


The arrival of generative AI technology has fostered a culture of additional uncertainty, while emerging as a necessary tool for supplementing the work of information security teams. Since human power can no longer scale to fully safeguard against the attack surface, AI has proven to provide valuable analysis and efficient threat detection capabilities to improve overall security posture.

However, concerns over data privacy, the threat of adversarial attacks, and the security aspects of AI are constantly evolving as the technology and understanding of ramifications evolves. Adopting a holistic approach to a cyber AI strategy to help your organisation understand the risks connected to AI technology may work most effectively, such as raising awareness, process implementation and training to prevent misuse is critical for a strong cyber strategy.

Understanding and minimising the risks associated with AI is much more than just avoiding potential harm. It is also about ensuring that these powerful technologies are used ethically and responsibly. With the right strategy and processes, the potential benefits could outweigh the challenges. Embracing and leveraging AI in security organisations could help lead to businesses becoming more adaptable, resilient, and forward-thinking.

Organisations should safely use the power of AI and LLMs by being wary of the possible drawbacks of this technology and taking pre-emptive measures to tackle them. AI tooling should be explored within platforms, but use of AI should not be relied on as the sole source of truth. By implementing robust policies and increasing user awareness, there is the potential to harness the power of AI for good.

If you want to learn more or discuss how leveraging AI for cyber could help your organisation, Crossword's Consulting Innovation Practice is here to help. Simply set up a convenient time to talk here.


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