What does AI security mean? - 10 tips to protect against AI risks

Artificial intelligence is changing the way companies work. AI systems support the analysis of large amounts of data, automate recurring tasks and enable new digital business models. However, the use of AI not only brings efficiency gains, but also new security-related challenges. Many companies have so far focused primarily on the functional benefits of AI and neglected the security of the systems used. However, the security of AI systems is becoming increasingly important in order to prevent malicious attacks.

This is precisely why the topic of AI security is becoming increasingly important. The term describes all measures aimed at protecting AI applications, underlying data and connected IT systems from attacks, manipulation and misconduct. Cybersecurity and cyber security play a central role in protecting both AI systems and the entire IT infrastructure against cyber attacks and modern threats. As AI systems often have access to sensitive company and customer data, security breaches can have significant economic, legal and reputational consequences.

With the growing proliferation of cloud-based AI services, generative AI tools and automated decision-making processes, the attack surface for cybercriminals is also increasing. The proliferation of powerful AI is a driving force for stricter and more accurate security controls and workflows. At the same time, many companies lack clear security concepts for dealing with artificial intelligence. AI security is therefore becoming a key issue for IT security and risk management in modern companies.

In this article, you will get an overview of what AI security means, the risks associated with the use of AI and why it is now crucial for companies to actively address the security of AI systems.

We also give you, as an AI user, 10 valuable tips to protect yourself from dangers!

What does AI security mean?

AI security refers to all technical, organizational and strategic measures aimed at protecting artificial intelligence systems from security risks. This includes protecting the AI models themselves as well as securing the data used, interfaces and connected IT infrastructures and the IT systems used as part of the infrastructure to be protected. The aim of AI security is to ensure the integrity, availability and confidentiality of AI-supported applications.

In contrast to traditional IT security, artificial intelligence brings with it new areas of attack. AI systems learn from data, make automated decisions and sometimes act independently. It is precisely these characteristics that make them vulnerable to targeted manipulation, incorrect training data or unauthorized access. AI security therefore starts with the development and training of AI models and accompanies the entire life cycle of an AI application.

Another key component of AI security is the protection of sensitive information. Many AI applications process personal data, business secrets or internal company information. Without suitable security measures, there is a risk that this data will be unintentionally disclosed, falsified or misused.

In summary, AI security ensures that AI systems can be operated reliably, traceably and securely. For companies, it is the prerequisite for using artificial intelligence responsibly in the long term without taking unnecessary security or compliance risks. Various aspects of AI security - technical, ethical, social and economic - must be considered holistically in order to ensure comprehensive protection.

As part of our AI consulting, we therefore attach particular importance to ensuring that all systems used meet the highest safety requirements.

Why AI security is becoming increasingly important for companies

Companies need to protect their data. However, the increasing use of artificial intelligence is fundamentally changing business processes, decision-making and the handling of data. At the same time, the dependency on AI systems, which are often deeply integrated into existing IT structures, is growing.

The integration of AI into security frameworks offers transformative benefits and strengthens companies' defenses in a measurable way.

Security gaps or malfunctions can therefore have a direct impact on business operations. In addition, AI applications often work with sensitive company or customer data, the protection of which is of great legal and economic importance. Attacks on AI models, manipulated training data or uncontrolled output from generative AI systems represent new risk scenarios that are not adequately covered by traditional security concepts alone. For companies, AI security is thus becoming a decisive factor in meeting compliance requirements, creating trust and ensuring the secure and sustainable use of artificial intelligence, whereby the relevance of data protection in connection with AI-supported products and their security must be particularly emphasized.

So that your company can benefit from the advantages of AI without having to compromise on security, with GPT4YOU we offer an AI solution that can be flexibly adapted to your requirements!

Typical dangers and risks when using AI

The use of artificial intelligence is associated with specific security risks that companies should be aware of and assess. The term AI security risks covers all threats and vulnerabilities that can arise in connection with the development, implementation and use of AI systems. These risks arise both from technical vulnerabilities and from the handling of data and models.

Typical risks include

• Manipulation of training data, leading to incorrect or distorted results

• Attacks on AI models in which the behavior of the systems is deliberately influenced

• Inadequately secured interfaces through which unauthorized access or data leaks are possible

• Disclosure of sensitive information by generative AI systems, for example through uncontrolled output

• Wrong decisions due to a lack of transparency if the results of AI systems are not traceable

Particular attention should be paid to the risks posed by generative AI. These models can be misused to create deepfakes, automated phishing attacks or even malicious code. The integration of generative AI models into security architectures therefore requires special protective measures to minimize new attack vectors. There is also the risk of model poisoning, where attackers manipulate the training data in order to compromise the integrity and reliability of the AI system.

Without suitable security measures, these risks can have a direct impact on business processes, data integrity and company reputation.

In order to sensitize employees to AI and its potential risks, the European Union prescribes mandatory training under the AI Act. Are your employees already sufficiently trained? If not, be sure to check out the FIDAcademy!

Which areas of the company are particularly affected?

AI security doesn't just affect the IT department, but has an impact on several areas of the company. Developers play a central role in designing, securing and monitoring AI systems by implementing specific guidelines and standards. The requirements and risks differ depending on the area in which the AI is used.

Particularly affected are

• IT and security departments, which are responsible for the secure operation, monitoring and protection of systems

• Specialist departments, such as sales, marketing, customer service or production, that actively use AI in their processes

• Data management and data science, as the quality, protection and use of data are crucial for secure AI systems

• Management and executive management, as strategic decisions, compliance requirements and liability issues are closely linked to the use of AI

A holistic approach to AI security ensures that all affected areas are involved and that risks can be identified and reduced at an early stage. Cooperation between different departments as well as external partners and organizations is essential in order to develop secure and trustworthy AI concepts. For this to succeed, we recommend AI consulting from experts!

The 5 core principles of AI security

We know that certain basic principles must be observed if artificial intelligence is to be used safely in a company. AI security is not limited to individual technical measures, but requires a holistic security concept across the entire life cycle of AI systems.

Principle #1: AI governance

A central element is clear AI governance. This includes defined processes for identifying and assessing AI risks as well as the integration of AI systems into existing information security and software development processes. This ensures that security aspects are already taken into account during planning and development.

Principle #2: Use proven IT security measures

AI applications are part of the existing IT landscape and should be protected accordingly with established IT security controls. Risk-based technical measures such as access controls, network segmentation or monitoring form the basis for reliable basic protection. Dedicated servers play a central role in ensuring business continuity in the event of emergency responses such as failover or zero-day exploits and detecting attacks at an early stage.

Principle #3: Security measures for data science and model development

Special requirements arise in the field of data science. Here, it is crucial to secure working environments, data flows and models in a targeted manner. Extensive and diverse data sets are essential for training secure ML models and improving the detection of anomalies and threats in ai security. This includes the validation of training data, regular testing of models and controlled development and test environments.

ML models play a central role in detecting anomalies and automating security processes by analyzing data in real time and identifying potential threats at an early stage.

Implementing robust AI data security processes can significantly reduce the risk of model poisoning.

Principle #4: Data minimization

Another core principle is to reduce the amount of data processed to what is necessary. This contributes significantly to data security, as limiting data usage reduces the risk of data misuse and unauthorized access. Both during development and during operation, the amount of data used and its storage and retention period should be limited in order to minimize potential risks.

Principle #5: Control AI behavior

Since AI systems can make decisions independently, continuous monitoring of their behavior is required. In addition, defense through AI-supported monitoring and adaptive defense mechanisms plays a central role in detecting and fending off threats in real time. Measures such as the principle of minimal assignment of rights, transparency, traceability of decisions and regular checks allow both unintentional errors and targeted manipulation to be detected at an early stage.

Companies that consistently implement these principles create a stable foundation for the secure use of artificial intelligence. This allows innovation potential to be exploited without jeopardizing the security of data, systems and business processes.

Do you lack the necessary expertise or specialist staff to implement these principles? Then contact us and we will support you with our AI consulting.

Advantages of AI Security

The integration of AI security into existing security concepts offers companies concrete advantages. The targeted use of AI allows security processes to be designed more efficiently and threats to be detected more quickly.

The key advantages of AI Security are

• Improved threat detection
  AI-supported security systems can analyze large amounts of data in a short time and detect anomalies that would be difficult to identify using traditional methods. AI security systems analyze huge data sets at machine speed and also identify subtle attack patterns. This means that even complex or previously unknown attack patterns can be detected at an early stage.

• Faster response times
  Automated security mechanisms make it possible to respond to threats almost in real time. The use of automation speeds up processes, reduces human intervention and significantly increases the efficiency of threat analysis and response to security incidents. This can significantly reduce the amount of time attackers remain undetected in the system.

• Scalability of security measures
  AI Security can be flexibly adapted to growing IT environments. Even with increasing data volumes or system complexity, a high level of security is maintained without having to increase personnel costs to the same extent.

• Adaptable protection mechanisms
  Modern AI security solutions are constantly evolving by learning from new attack patterns. This enables them to respond to new threats for which no fixed rules or signatures yet exist.

Challenges and measures in the area of AI security

Despite the advantages, the use of artificial intelligence also brings with it specific security challenges. These require targeted technical and organizational measures.

The key challenges include

• Targeted manipulation of AI systems
  Attackers can attempt to deceive AI models with specially prepared inputs and cause incorrect decisions to be made. Targeted training with manipulated examples can increase the resilience of the models.

• Manipulation of training data
  If training data is falsified or compromised, this can have long-term effects on the behavior of AI systems. Clear data security processes and controlled training environments help to minimize this risk.

• Limited traceability of decisions
  Complex AI models are often difficult to interpret. A lack of transparency makes safety assessments and compliance with regulatory requirements more difficult. Explainable AI approaches help to make decision-making processes easier to understand.

• Data protection and compliance risks
  AI applications often require large amounts of data. Without suitable protection mechanisms, this can lead to data protection problems. Techniques for data-saving processing help to better protect sensitive information.

A holistic approach is required to meet these challenges. This combines technical security measures with clear organizational guidelines and a well thought-out governance structure. This is the only way to anchor AI security in the company sustainably and effectively, and we will support you in the process.

Types of AI security systems and how they work

AI security comprises different types of systems, each of which is designed to meet specific security requirements. As modern security solutions, AI security systems rely on artificial intelligence to detect, analyze and defend against threats that are difficult to identify using traditional methods.

Network detection and response (NDR)

AI-supported NDR solutions continuously analyze network traffic and identify unusual activities that may indicate a security incident. The internet is a central interface for attacks, which is why monitoring incoming and outgoing connections is particularly important in order to detect and ward off threats at an early stage. For this purpose, normal communication patterns in the network are learned and used as a reference. Deviations from these patterns are automatically detected and flagged for further analysis. The implementation of AI in network security ranges from the identification of suspicious external connections to the implementation of stricter network segmentation. In this way, attacks that do not use known signatures can also be detected.

User and Entity Behavior Analysis (UEBA)

UEBA systems focus on the behavior of users, devices and applications. They specifically analyse the behaviour of different device types, such as Linux or Microsoft devices, in order to better detect and prioritize attacks on specific devices. Typical behavioral patterns are defined and continuously checked with the help of machine learning. Noticeable deviations can indicate compromised access or internal security risks. This approach offers high added value, particularly when it comes to detecting insider threats.

Advanced detection and response (XDR)

XDR solutions bring together security information from different systems and sources. By correlating this data and using AI, complex attack scenarios that span multiple systems or longer periods of time can be detected. This gives companies a holistic view of security incidents and enables them to respond in a more targeted manner.

Securing AI models

In addition to securing the IT infrastructure, protecting the AI models themselves is also becoming increasingly important. Specialized security solutions monitor inputs, outputs and the behaviour of models in order to detect attempts at manipulation or changes to training data at an early stage. This ensures that AI systems work in a reliable and controlled manner.

Email security and AI - new risks and protective measures

Email communication remains a key tool for companies - and therefore also a popular target for cyber attacks. The advent of artificial intelligence in IT security opens up completely new opportunities in the field of email, but also risks. Modern AI systems analyze incoming and outgoing emails in real time, detect suspicious patterns and can thus identify phishing attempts, malware attachments or social engineering attacks at an early stage. Companies benefit from AI-supported solutions that continuously learn from new threats and adapt dynamically.

However, the intelligence of the systems is also used by attackers: AI can be used to create deceptively real, personalized phishing emails that easily bypass traditional filters. Such attacks are often difficult to detect and can cause considerable damage if they are not fended off in time. It is therefore crucial that companies not only rely on the latest AI security solutions, but also regularly sensitize employees to the dangers.

In the area of email security, the use of AI-based protection mechanisms that automatically detect and block suspicious activities is recommended. In addition, clear guidelines for handling emails, regular training and close cooperation between IT security and specialist departments should be established. In this way, the benefits of AI systems can be optimally exploited while minimizing the risks of new, intelligent attacks.

Deep learning and AI security - opportunities and challenges

Deep learning has established itself as a key technology in the field of artificial intelligence and is revolutionizing the way companies protect their IT systems. By using deep learning algorithms, AI systems can analyze huge amounts of data in real time and detect even complex, previously unknown threats. Particularly in the area of network and endpoint security, deep learning enables precise identification of anomalies that often remain undetected using conventional methods.

For companies, this means that attacks can be detected and averted more quickly, IT systems are more secure and response times to new threats are significantly reduced. At the same time, however, the complexity of the AI systems used is growing. Deep learning models are often difficult to understand and can themselves become the target of attacks - for example through the targeted manipulation of training data or adversarial attacks, in which attackers attempt to deliberately deceive the algorithms.

In order to make the most of the opportunities offered by deep learning in the field of AI security, companies should rely on robust protection mechanisms that guarantee both the integrity of the data and the security of the algorithms. This includes regular checks of the models, the use of explainable AI methods and continuous adaptation to new threat situations. In this way, the potential of deep learning for IT security can be fully exploited without incurring new risks.

AI safety and its future - trends and outlook

The future of AI security is characterized by rapid technological development and a constantly growing importance for companies in all sectors. With the increasing use of AI systems in areas such as industry, finance and healthcare, the complexity of the threat situation is also increasing. Companies are faced with the challenge of continuously adapting their IT security to new attack vectors and innovative attack methods.

A key trend is the increased use of AI-supported security solutions that not only react to known patterns, but can also recognize new, previously unknown threats. Technologies such as Explainable AI (XAI) ensure that decisions made by AI systems remain comprehensible and verifiable - an important aspect for compliance and trust. Transfer learning, in which AI models transfer knowledge from one area of application to another, also opens up new possibilities, but also harbors additional risks, such as unexpected interactions.

For companies, this means that AI security must be understood as a dynamic process that requires continuous adaptation and innovation. The integration of modern AI solutions, employee training and close cooperation between IT, specialist departments and management are crucial to ensure protection against new threats. This is the only way to exploit the full potential of artificial intelligence safely and responsibly - today and in the future.

AI security - security as the key to successful AI deployment

Artificial intelligence offers companies enormous opportunities - from more efficient processes and more precise analyses to completely new business models. At the same time, the use of AI entails specific security risks that go far beyond traditional IT threats. Incorrect data, manipulation, uncontrolled system outputs or unauthorized access can quickly lead to financial, legal or reputational damage.

AI security is therefore not an optional extra, but a central component of responsible AI deployment. It ensures that systems work reliably, results remain traceable and sensitive data is protected. For companies, this means that anyone using AI must also use it securely - from the selection of data to model monitoring and user training.

At FIDA, AI security is an integral part of our AI consulting and training. We support companies in identifying security risks at an early stage, securing processes and enabling employees to use AI responsibly. In this way, innovation potential can be optimally exploited without security and compliance falling by the wayside.

With a clear focus on AI security, you can ensure that AI systems in your company are not only efficient, but also reliable and secure in daily use.