What is AI security?

AI security is a branch of cybersecurity specific to AI systems. It refers to the set of processes, best practices, and technology solutions that protect AI systems from threats and vulnerabilities.

AI security is important for several reasons, including:?

• Protection of sensitive data. AI systems process vast amounts of sensitive data including financial, medical, personal, and financial information.?

• Maintaining system integrity. Unchecked vulnerabilities in AI systems can lead to compromised models, which in turn may yield inaccurate or harmful outcomes.

• Safeguarding the availability of AI services. Like any other service, AI systems must remain available and operational, especially as more people and organizations become reliant on them. Security breaches often result in downtime which can disrupt essential services.?

• Accountability. For AI to be adopted on a global scale, people and organizations need to trust that AI systems are secure and reliable.

Key concepts in AI security

• Confidentiality: Ensuring that sensitive data is accessible only to authorized individuals or systems.?

• Integrity: Maintaining the accuracy and consistency of the AI systems.

• Availability: Ensuring that AI systems remain operational and accessible.?
• Accountability: The ability to trace actions made by AI systems.

AI security vs. AI for cybersecurity

It's important to distinguish between two related but different concepts: AI security and AI for cybersecurity.

AI security focuses on the protection of AI systems themselves. It’s security for AI that encompasses the strategies, tools, and practices aimed at safeguarding AI models, data, and algorithms from threats. This includes ensuring that the AI system functions as intended and that attackers cannot exploit vulnerabilities to manipulate outputs or steal sensitive information.

AI for cybersecurity, on the other hand, refers to the use of AI tools and models to improve an organization's ability to detect, respond to, and mitigate threats to all its technology systems. It helps organizations analyze vast amounts of event data and identify patterns that indicate potential threats. AI for cybersecurity can analyze and correlate events and cyberthreat data across multiple sources.

In summary, AI security is about protecting AI systems, while AI for cybersecurity refers to the use of AI systems to enhance an organization’s overall security posture.

Common AI security threats

As AI systems become more widely used by companies and individuals, they become increasingly attractive targets for cyberattacks.

Several key threats pose risks to the security of AI systems:?

Data Poisoning

Data poisoning occurs when attackers inject malicious or misleading data into an AI system's training set. Since AI models are only as good as the data they are trained on, corrupting this data can lead to inaccurate or harmful outputs.

Model inversion attacks

In model inversion attacks, attackers use an AI model's predictions to reverse engineer sensitive information that the model was trained on. This can lead to the exposure of confidential data, such as personal information, that was not intended to be publicly accessible. These attacks pose a significant risk, especially when dealing with AI models that process sensitive information.

Adversarial attacks

Adversarial attacks involve creating deceptive inputs that trick AI models into making incorrect predictions or classifications. In these attacks, seemingly benign inputs, like an altered image or audio clip, cause an AI model to behave unpredictably. In a real-world example, researchers demonstrated how subtle alterations to images could fool facial recognition systems into misidentifying people.

Privacy concerns?

AI systems often rely on large datasets, many of which contain personal or sensitive information. Ensuring the privacy of individuals whose data is used in AI training is a critical aspect of AI security. Breaches of privacy can occur when data is improperly handled, stored, or used in a way that violates user consent.

Rushed Deployments?

Companies often face intense pressure to innovate quickly, which can result in inadequate testing, rushed deployments, and insufficient security vetting. This increase in the pace of development sometimes leaves critical vulnerabilities unaddressed, creating security risks once the AI system is in operation.

Supply chain vulnerabilities?

The AI supply chain is a complex ecosystem that presents potential vulnerabilities that could compromise the integrity and security of AI systems. Vulnerabilities in third-party libraries or models sometimes expose AI systems to exploitation.?

AI misconfiguration

When developing and deploying AI applications, misconfigurations can expose organizations to direct risks, like failing to implement identity governance for an AI resource, and indirect risks, like vulnerabilities in an internet-exposed virtual machine, which could allow an attacker to gain access to an AI resource.?

Prompt injections?

?In a prompt injection attack, a hacker disguises a malicious input as a legitimate prompt, causing unintended actions by an AI system. By crafting deceptive prompts, attackers trick AI models into generating outputs that include confidential information.?

Best practices for securing AI systems

Data security

Model security

Access control

Regular audits and monitoring

Enhance AI security with the right tools

Security frameworks

Encryption techniques?

AI security tools

Emerging trends in AI Security

As AI becomes more prevalent, the threats to these systems will continue to grow more sophisticated. One major concern is the use of AI itself to automate cyberattacks, which makes it easier for adversaries to conduct highly targeted and efficient campaigns. For instance, attackers are using large language models and AI phishing techniques to craft convincing, personalized messages that increase the likelihood of victim deception. The scale and precision of these attacks present new challenges for traditional cybersecurity defences.

In response to these evolving threats, many organizations are starting to employ AI-powered defense systems. These tools, like Microsoft’s AI-powered Unified SecOps platforms, detect and mitigate threats in real time by identifying abnormal behaviour and automating responses to attacks.

AI security solutions

Modern AI security solutions that secure and govern AI significantly enhance an organization's protection against these new threats. By integrating these powerful AI security solutions, organizations can better protect their sensitive data, maintain regulatory compliance, and help ensure the resilience of their AI environments against future threats.

Microsoft Purview

Discover, classify, and label sensitive data in your environment.

Microsoft Entra

Ensure that the right identities have appropriate access to the right AI apps at the right time.

Azure AI Studio

Continually assess and improve the quality and safety of your generative AI applications.

Microsoft Defender

Detect and control shadow AI, vulnerabilities, and AI components across multi-cloud environments.

Microsoft Intune

Protect your corporate data in Copilot across managed and unmanaged devices.

Azure AI Content Safety

Detect and block prompt injection attacks to secure AI applications.

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