Artificial Intelligence (AI) in cyber security: What’s next?

The digital landscape is expanding at an unprecedented pace, which means the threat landscape in cyberspace is evolving just as rapidly. Technological advancements have resulted in the growing capabilities of malicious actors seeking to exploit vulnerabilities for personal gain, political motives, or simply to wreak havoc. In the ongoing battle between cybersecurity professionals and cyber threats, artificial intelligence (AI) has emerged as a powerful ally, revolutionizing the way we defend our digital frontiers.

Recent research from Blackberry reveals that 82% of IT decision-makers plan to invest in AI-driven cybersecurity in the next two years as the concern for cybersecurity in an age of AI rises, The AI cybersecurity market is growing at an incredible rate and is expected to reach a market value of $46.3 billion in 2027, but what impact will it have? And how can businesses prepare?

We explore the future of AI in cybersecurity, covering everything from the uses of AI in cybersecurity all the way through to the ethical considerations associated.

What’s in this article?

• Executive summary
• 6 Uses of AI in cyber threat detection and prevention
• How does AI increase the sophistication of cyber security threats?
• What challenges do we face in the wake of AI in cybersecurity?
• Building out AI-trained teams to combat the challenges of AI in cybersecurity

Executive summary

While artificial intelligence (AI) has the potential to amplify cyber threats, it currently plays a huge role in defending against cyber security threats. AI is having a transformative impact on cyber threat detection, highlighting its prowess in real-time threat detection, behavioral analytics, machine learning for endpoint security, adaptive defence mechanisms, and predictive analytics. However, the same technology can also be exploited by cybercriminals, optimising attacks, automating malware creation, enabling impersonation, poisoning data sets, and facilitating unauthorised access to sensitive data.

As more organisations embrace AI, they begin seeing the benefits, yet this is also met with significant challenges. Adversarial attacks manipulate AI models, biases in training data raise fairness concerns, and the opacity of AI decision-making poses transparency issues. Data privacy considerations, over-reliance on automation, integration complexity, and resource intensiveness further complicate the AI cybersecurity landscape. To address these challenges, building AI-trained teams becomes paramount. The fusion of human expertise with AI capabilities ensures adaptability, ethical considerations, and effective responses to the evolving cyber threat landscape.

6 Uses of AI in cyber threat detection and prevention

As cyber threats become more sophisticated, organisations are turning to AI as a powerful tool to combat it. It’s not just being used to augment, but revolutionise the way we detect and prevent cyber threats, providing a level of adaptability and insight that traditional methods struggle to match.

1. Real-time threat detection

Traditional cybersecurity measures often rely on predefined rules to identify potential threats. However, these static rulesets can fall short in the face of rapidly evolving attack techniques. AI, particularly machine learning algorithms, excels in real-time threat detection by continuously analysing vast amounts of data to identify patterns indicative of potential threats. This enables organisations to detect anomalies and potential security breaches as they happen, providing a critical edge in the race against cybercriminals.

2. Behavioral analytics

AI’s ability to understand and adapt to normal system and user behaviour is a game-changer. Behavioural analytics powered by AI can establish a baseline for typical activities on a network, allowing the system to quickly identify deviations that might signify a security threat. Whether it’s an unusual login location or a sudden spike in data access, AI-driven systems can recognise these anomalies and trigger alerts, enabling swift responses to potential threats.

3. Machine learning in endpoint security

Endpoints, such as individual devices, are often targeted by cybercriminals seeking entry points into a network. AI, through machine learning models, enhances endpoint security by recognising patterns associated with known malware and, importantly, by identifying previously unseen threats. This capability, often referred to as zero-day threat detection, is crucial in an environment where attackers are constantly developing new tactics to bypass traditional security measures.

4. Adaptive defence mechanisms

One of the strengths of AI in cybersecurity is its adaptability. Unlike static rule-based systems, AI can evolve and learn from new data. This adaptability is particularly valuable in countering the ever-changing tactics of cybercriminals. As threats evolve, AI systems can dynamically adjust their defence mechanisms, providing a more robust and flexible line of defence against emerging vulnerabilities and attack vectors.

5. Predictive analysis for proactive defence

AI’s analytical capabilities extend beyond real-time detection to predictive analysis. By examining historical data and identifying emerging trends, AI can anticipate potential threats. This proactive approach allows organisations to bolster their defences before an attack occurs, reducing the risk and potential impact of cybersecurity incidents.

6. Reducing false positives

One common challenge in traditional cybersecurity is the occurrence of false positives – security alerts that are triggered but do not indicate an actual threat. AI can significantly reduce false positives by fine-tuning its analysis based on context and learning from the organisation’s specific environment. This not only enhances the accuracy of threat detection but also reduces the burden on cybersecurity teams by focusing their attention on genuine threats.

How does AI increase the sophistication of cyber security threats?

On the flipside, while AI is making major moves in cyber threat detection and prevention, AI technology can also be used for malicious purposes. This contributes to actually increasing cyber security threats and is being used to commit fraud and scam people in the following ways:

1. Optimising cyber attacks

As AI capabilities grow, cybercriminals are leveraging this technology to optimise and fine-tune their cyber attacks. It enables attackers to efficiently analyse vast datasets to identify vulnerabilities and weaknesses in target systems. Using automated penetration testing, AI can systematically probe networks, seeking the most effective entry points for exploitations.

2. Automating malware

Similarly, AI is being used to automate the creation and deployment of malware, significantly accelerating the pace at which new threats are introduced. Machine learning algorithms work to analyse patterns in existing malware to generate variants to evade traditional detection systems or customise malware based on the target environments, which is why it’s imperative that you upgrade your detection technology!

3. Enabling impersonation

AI’s ability to mimic human behaviour is harnessed by cybercriminals to enhance impersonation attacks. Advanced AI algorithms can analyse and replicate the communication styles of individuals, enabling the creation of sophisticated phishing emails or social engineering campaigns.

4. Poisoning data sets

Training data sets are crucial for developing effective threat detection models. Malicious actors can exploit this dependency by injecting poisoned data into the training process, leading to biased or compromised models. This tactic can result in AI systems that are either more susceptible to certain types of attacks or less effective in identifying specific threats.

5. Accessing sensitive data

Machine learning algorithms can analyse patterns of user behaviour and system interactions to identify potential weaknesses in security protocols. This enables cybercriminals to execute more targeted and sophisticated attacks, such as credential stuffing or password guessing, with a higher likelihood of success.

What challenges do we face in the wake of AI in cybersecurity?

While artificial intelligence (AI) has emerged as a powerful ally in the realm of cybersecurity, its integration is not without its challenges and potential threats. As organisations embrace AI to fortify their defences against cyber threats, they must navigate a landscape of complexities and risks. Understanding these challenges is crucial to harnessing the full potential of AI while mitigating the associated threats.

Some of the key challenges businesses currently face include:

Adversarial attacks

In the context of cybersecurity, adversarial attacks refers to the intentional manipulation of AI models by malicious actors. By subtly altering input data, attackers can deceive AI systems into misclassifying information or making incorrect decisions. This poses a significant threat, especially in scenarios where AI is relied upon for critical decisions, such as in autonomous systems or security protocols.

Bias and fairness concerns

AI models are trained on historical data, which may inadvertently contain biases. When applied to cybersecurity, biased models may lead to discriminatory outcomes, favouring certain types of threats or neglecting others. Ensuring fairness in AI cybersecurity is essential to avoid unintentional discrimination and to provide comprehensive protection across all potential threats.

Lack of explainability

Many AI models, especially deep learning algorithms, operate as complex black boxes, making it challenging to interpret their decision-making processes. In cybersecurity, the inability to explain why an AI system flagged a particular activity as a threat can be a significant hurdle. The lack of transparency can impede trust and hinder the ability of cybersecurity professionals to understand and validate AI-driven alerts.

Data privacy concerns

AI systems often require vast amounts of data for training and continuous learning. This raises concerns about the privacy and security of sensitive information. Striking a balance between leveraging data for effective threat detection and respecting user privacy is a delicate challenge that organisations must navigate.

Over-reliance and deskilling

Over-reliance on AI in cybersecurity without proper human oversight can be a double-edged sword. Depending too heavily on automated systems may lead to complacency, as human intuition and expertise are still crucial in understanding the broader context of threats. Additionally, overreliance may contribute to the decimation of cybersecurity professionals, making them less capable of responding to novel or sophisticated attacks without AI assistance.

Integration complexity

Implementing AI solutions into existing cybersecurity infrastructure can be a complex process. Ensuring seamless integration, compatibility with existing systems, and avoiding disruptions to ongoing operations require careful planning. The challenge lies in adapting AI tools to work cohesively with diverse technologies, protocols, and security architectures.

Resource intensiveness

Training and maintaining sophisticated AI models demand substantial computational resources. For smaller organisations with limited budgets, this can be a barrier to entry. The resource-intensive nature of AI in cybersecurity may create a divide, with well-funded entities benefiting disproportionately from advanced AI-driven defences.

Building out AI-trained teams to combat the challenges of AI in cybersecurity

Hiring or upskilling to ensure you have the right trained staff is not just beneficial but essential in effectively tackling the challenges posed by the integration of AI in your cybersecurity operations. The complexity of AI models, potential biases, and the need for interpretability demand a workforce with a deep understanding of both cybersecurity principles and the intricacies of artificial intelligence. Trained staff brings the expertise required to navigate the nuances of AI-driven threat detection, interpret the context surrounding alerts, and address the ethical considerations inherent in leveraging advanced technologies.

The experiences of human cybersecurity experts provides a crucial layer of adaptability, enabling rapid responses to emerging threats and the ability to validate and refine AI-generated alerts. The collaboration between skilled cybersecurity professionals and AI ensures a balanced approach, where the strengths of both human intuition and machine processing power are harnessed to create a resilient defence against an ever-expanding array of cyber threats.

Hire the top cybersecurity talent trained in AI with FDM

The requirement to handle vast amounts of data and devices has meant cybersecurity has outgrown a human-first approach, but this does not discount the need for cybersecurity talent. Now’s the time to equip your organisation with the finest cybersecurity talent, trained in the latest AI technologies and ready to protect your operations from malicious cyber threats. At FDM, we provide our clients with top IT Security Consultants to help protect your organisation’s data.

Learn more about our IT operations Consulting services or get in touch for more information.