AI Security Risks: Identifying and Mitigating Threats

It’s no secret that AI has a strong prevalence in modern companies. With 55% of businesses currently using AI, and another 45% exploring the technology for future implementation, it’s clear that AI is here to stay.

While it has transformed business operations and unlocked remarkable capabilities, AI has also introduced a complex web of security challenges that organisations must navigate carefully to protect their assets and maintain trust.

Discover in this article the critical AI security risks that you need to know, their impact on businesses, and best practices for mitigating these threats.

The Growing Impact of AI Security Risks

AI security risks encompass the vulnerabilities and threats that emerge from implementing and using artificial intelligence systems. These risks can manifest in various forms, from sophisticated data breaches to subtle model manipulations.

As AI becomes more integrated into business functions, the potential for security breaches and malicious exploitation increases substantially. The global average cost of a data breach rose nearly 10% to $4.9 million in 2024 —a stark reminder of the financial stakes involved when AI security measures fall short.

With AI systems handling sensitive data and critical operations, ensuring robust security measures is imperative for organisations. Understanding these risks is crucial to safeguarding AI-driven processes and maintaining the integrity of operations.

Understanding the Top AI Security Threats

1. Data Breaches

At the core of AI security lies the challenge of safeguarding sensitive data from unauthorised access or breaches. Data breaches can occur through unauthorised access, insufficient data encryption, or vulnerabilities in the AI infrastructure. Attackers may exploit these weaknesses to steal sensitive information processed or stored by AI models, leading to significant financial and reputational damage.

To protect against breaches, a multi-layered approach combining robust encryption protocols, strict access controls, and regular security audits is essential. Ensuring that only authorised personnel can access sensitive data and continuously monitoring for unusual activities are also essential measures to mitigate the risk of data breaches.

2. Adversarial Attacks & Data Poisoning

AI models face two primary threats: adversarial attacks and data poisoning. Adversarial attacks involve manipulating input data in subtle ways to mislead models into producing incorrect or harmful outputs. Data poisoning, on the other hand, corrupts the training process by introducing malicious data, which skews the model’s learning and leads to biased or compromised outputs. Both threats pose significant risks to the performance and reliability of these systems, undermining trust in AI-driven decisions.

To defend against these threats, organisations can use techniques such as adversarial training, where models are exposed to manipulated inputs during development to improve their resilience. Robust data validation processes, regular dataset audits, and secure data storage solutions can also help to ensure data integrity. By maintaining high standards for data quality and monitoring for unusual patterns, organisations can maintain the reliability of their AI models and safeguard against manipulation.

3. Model Theft

Model theft occurs when unauthorised parties gain access to and replicate an organisation's AI models. This not only leads to the loss of intellectual property, but can also affect the organisation’s competitive advantage and the potential misuse of the stolen models by malicious actors.

Beyond security concerns, model theft can result in significant legal and financial repercussions. Organisations may face lawsuits, regulatory fines, and significant losses as their proprietary technology is devalued. These costs of rebuilding and securing compromised models can further strain resources.

To prevent model theft, organisations should implement strong access controls, encrypt model storage and transmission, and use techniques like watermarking to trace unauthorised copies. Continuous monitoring for unusual access patterns can also help detect and mitigate potential breaches.

4. Supply Chain Risks

AI supply chains can be complex, involving multiple vendors and third-party services. Each link in the supply chain presents potential vulnerabilities that attackers can exploit to compromise the integrity, security, and reliability of the entire AI system.

Mitigating these risks requires the thorough vendor vetting, clear security protocols, and ongoing transparency across the supply chain. Regular security assessments and collaboration with suppliers to identify and address potential weaknesses are also essential strategies for mitigating supply chain risks.

5. Automated Malware Generation

In the hands of malicious actors, AI can be used to automate the creation of sophisticated malware, enabling cybercriminals to develop and deploy threats at unprecedented scale. This rapid automation increases the speed, efficiency, and adaptability of attacks, making them harder to detect and mitigate.

To defend against AI-generated malware, organisations must leverage advanced threat detection systems, employ behaviour-based analysis, and ensure security software remains up to date. Educating employees about emerging malware threats can also provide a first line of defence, helping to prevent successful attacks.

6. Privacy and Surveillance Concerns

AI systems often process large amounts of personal data, creating significant privacy and data protection challenges.  When this data falls into the wrong hands through unauthorised access or misuse, organisations face not just privacy violations but a fundamental breach of customer trust.

To safeguard against these risks, organisations should implement robust data protection policies, comply with relevant regulations, and use techniques like data anonymisation and encryption. Regular privacy assessments and audits can also ensure ongoing compliance and safeguard sensitive information, both verifying that privacy controls work effectively and identifying potential vulnerabilities before they can be exploited.

Strategies for Mitigating AI Security Risks

1. Implementing Robust Data Handling and Validation

Securing data handling and thorough validation are essential for mitigating AI security risks. Organisations should encrypt their data, enforce strict access controls, and conduct regular audits to ensure data integrity and security.

2. Limiting Application Permissions for AI Tools

Restricting the permissions granted to AI tools can help reduce the potential attack surface. By adhering to the principle of least privilege, organisations can limit the access AI systems have to sensitive data and critical resources, thereby minimising the risk of unauthorised actions.

3. Ensuring Model Transparency and Explainability

Transparent and explainable AI models facilitate better understanding and trust, making it easier to identify and address security vulnerabilities. Organisations should prioritise developing models that provide clear reasoning for their decisions, enhancing accountability and security.

4. Continuous Monitoring and Incident Response

Continuous monitoring of AI systems allows organisations to detect and respond to security incidents in real time. According to Lakera, two-thirds of organisations now use security AI and automation in their security operations centers, a 10% increase from the previous year. A well-designed incident response plan also ensures that threats are addressed swiftly and effectively.

5. Adopting a Zero Trust Security Framework

A Zero Trust approach assumes that threats can originate both inside and outside the network, requiring strict verification for every access request. Adopting this approach can help organisations to security posture and reduce the risk of unauthorised access to AI systems.

6. Aligning with Recognised Governance Frameworks

Following established security frameworks helps organisations systematically address AI-related risks and strengthen overall security. These frameworks provide structured guidelines for managing threats and ensuring compliance with industry standards:

• NIST AI Risk Management Framework (RMF) - provides guidelines for identifying and managing AI-related risks, helping organisations strengthen their AI measures.
• ISO/IEC 27001 - offers a comprehensive approach to information security management, enabling organisations to establish effective security controls and processes to protect their AI systems from potential risks.

Regular reviews and audits are also essential for maintaining robust AI security. By continuously assessing their security measures and compliance with security frameworks, organisations can ensure their AI systems remain secure over time.

The CISO's Role in AI Security

Chief Information Security Officers (CISOs) play a crucial role in integrating AI risk management with organisational security policies by collaborating with cross-functional teams, including IT, legal, and compliance departments, to address AI-specific challenges effectively.

Their success relies heavily on effective board communication, where presenting AI risk scenarios and demonstrating the return on investment (ROI) for mitigation strategies in business terms helps secure support for AI security initiatives.

To measure the effectiveness of these security strategies, organisations track key performance indicators (KPIs) related to AI security, such as breach prevention rates and time-to-detect incidents, enabling them to make informed decisions to enhance their defences.

Additionally, CISOs must set a strategic tone for adopting governance frameworks like NIST RMF or ISO/IEC 27001, which is vital for aligning AI security efforts with broader organisational goals and ensuring that AI security measures are integrated seamlessly into the overall security strategy.

Future Trends in AI Security: What’s Next?

The rise of generative AI, which creates new content based on existing data, has introduced unique security challenges to organisations.

These AI systems can be exploited to generate misleading information or sophisticated attack vectors, complicating traditional security measures. As AI technology continues to evolve, so do the threats it poses, making it essential to proactively develop defence measures, such as advanced threat detection and response systems, to stay ahead of emerging AI-driven cyber threats.

The significance of this challenge is reflected in market trends. According to AllAboutAI, the AI cybersecurity market, valued at $24.3 billion in 2023, is projected to double by 2026 and reach nearly $134 billion by 2030. This remarkable growth underscores both the increasing importance of AI in enhancing cybersecurity defences and the pressing need to address the accompanying security risks.

In such a complex landscape, proactive security measures aren't optional—they're essential for safeguarding your assets, maintaining trust, and ensuring the continued success of AI-driven initiatives.