AI is rapidly transforming industries, but the increasing reliance on AI systems brings new and complex cybersecurity challenges. These challenges call for a proactive approach to safeguarding AI systems, their infrastructure and data.
Cybersecurity assurance for any technology can be complex to implement. Add AI to the mix and you need to pay attention not only to those components but the surrounding environment. Here’s a simple guide to covering most of your bases.
Know your assets
The initial step is to identify and categorize all assets based on their importance. This assessment considers factors such as business value, potential impact of compromise and exposure to threats.
Using automated tools like CMDB (Configuration Management Database) or cloud-native asset discovery solutions is a best practice for mapping all AI-related assets efficiently.
Know your enemy
Effective security assurance for AI-based systems is built on a foundation of continuous risk assessment and threat modeling. It’s an essential throughout the entire lifecycle. If you don’t have a strategy for this, it’s like treating symptoms rather than the underlying problem.
Risk assessment may be challenging without clear guidance or previous experience. However, leveraging threat modeling techniques like STRIDE, PASTA, or DREAD can provide a structured approach to identifying potential threats to the system. Some of the frameworks that may simplify threat identification and provide process insights are
MITRE ATLAS ,
Google Secure AI Framework,
NIST AI Risk Management Framework and
OWASP LLM Top 10.
Risk Assessment
Once risks are identified, the process proceeds to estimation and evaluation. This stage is about understanding the potential impact (e.g., technical, financial, operational, reputational) and the likelihood of the identified risks. To evaluate the technical severity of security threats, try the
NIST CVSS (Common Vulnerability Scoring System) as an industry standard.
Subsequent action involves risk treatment – specifically, developing and implementing a strategy for its resolution. This means considering options like risk avoidance, mitigation, transfer, or acceptance, with the future selecting appropriate security controls for managing risk to an acceptable level. Strong security relies on ongoing monitoring of controls and new threats, along with regular risk assessments that adjust to changes in AI systems.
Risk Sources
Where do AI security risks arise more often? If we can identify the source, that will go a long way to keeping data and systems safe. Weak points can appear in a variety of ways from noting the complexity of the environment, not creating transparent working practices, lack of attention to data quality, not keeping up your hardware and rushing in with new, untested products.
Each stage of the development lifecycle presents unique challenges to reliability and performance. The list of known AI security threats can be found in the
MIT AI risk repository, currently, it contains over 1,000 documented AI risks with new information updated regularly.
Secure the Lifecycle
Ensuring cybersecurity in AI requires a holistic approach that spans the entire lifecycle – from data preparation and model development to deployment and ongoing maintenance. The diagram below illustrates a simplified AI software development process, along with the relevant regulatory entities aligned with ISO 27001.
Securing the Foundation: Data
Training AI systems require vast amounts of data, potentially including personal information, which conflicts with privacy principles like transparency, consent, and purpose specification.
Data Leakage
AI can infer and disclose sensitive information – data leakage. This can be triggered by adversarial attacks revealing memorized data or through the model’s capacity to draw connections between disparate information sources. Even inaccurate guesses made by the models can be damaging, particularly if they involve sensitive topics or lead to unfair treatment.
Data Poisoning
The other vector of attack is injecting malicious data into the training dataset to manipulate the model’s behavior, a technique known as data poisoning. This threat, identified as #4 of OWASP LLM TOP 10 framework, can lead to biased, inaccurate, or even harmful AI decisions. Such attacks may involve inserting subtle manipulations to skew predictions, introducing backdoors that activate under specific conditions, or corrupting the model’s generalization ability.
Data Processing
The core priority during the data processing flows is to guarantee the security of all communication channels between AI system components. Encrypting these channels ensures that data exchanged within the system remains confidential. Access to raw data should be controlled by implementing role-based access control (RBAC).
In certain scenarios, isolating the model and its data within dedicated network segments provides an additional layer of protection. Encryption extends beyond communication to encompass data at rest. Employing robust standards like AES-256 for stored data and TLS for data in motion is a required practice. Finally, when data is no longer needed, secure disposal practices must be enforced to make sure that it is irretrievably destroyed, preventing any possibility of unauthorized recovery or misuse.
Data Transformation
Data transformation requires careful consideration of security and privacy, whether cleaning, normalizing, or augmenting data for that reason specific protection strategies may be employed.
Securing AI Models
Securing AI isn’t just about protecting data, it’s about all parts of the AI chain and it’s worth having an end-to-end idea of how to secure it. Keep in mind how you manage aspects like feature engineering, algorithm selection, deployment, backups and restoration and of course, security misconfiguration. Nobody is perfect, but it does pay to get ahead with all aspects and understand how you will mitigate risks or avoid security breaches.
There are many other IT security aspects to consider when deploying AI systems. These are the basics of your cybersecurity planning and shouldn’t be ignored when applying a new technology.
- Consider the authentication and authorization mechanism, implement multi-factor authentication (MFA), utilize strong password policies, and regularly rotate credentials.
- Software development security – implement secure coding best practices and standards, such as OWASP and NIST.
- Make code reviews and security testing an inevitable part of the development process. Regularly scan system components for vulnerabilities using automated tools.
- Maintain version control for the configuration scripts and deployments to track changes, enable rollbacks, and analyze vulnerabilities.
- Network Security – AI models often interact with external systems via APIs so make sure the usual protections are in place from firewalls to intrusion detection/prevention systems (IDS/IPS) and analyze network traffic for malicious activity.
- Define a secure update process that prevents the introduction of new vulnerabilities during model retraining.
- Develop and implement an incident response plan specifically for AI model security incidents.
The People Problem
While technical security measures are fundamental to protecting AI systems, the human element remains a significant factor in cybersecurity. Studies show that human error contributes to over
88% of data breaches. Even the most robust security frameworks can be undermined by misconfigurations, social engineering attacks, or lack of proper training.
By fostering a culture of shared responsibility, organizations can effectively mitigate risks, build resilience against evolving threats, and unlock the full potential of AI technologies.
Securing AI ecosystems is not just an IT challenge, it’s a multifaceted endeavor demanding a comprehensive strategy.
As more of us use AI across many industries, the consequences of security breaches can be far-reaching, impacting not only businesses but also individuals and society as a whole. The future of AI hinges on our ability to build trust and confidence in these technologies, and cybersecurity plays a pivotal role in achieving this goal.
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