The Role of AI Security Governance in Strengthening Incident Response Plans

AI security governance has become essential for organisations using artificial intelligence (AI) solutions in their operations. AI tools and technologies are used in almost every industry to automate tasks and save plenty of time and resources.

But, AI solutions come with many challenges, such as biases in algorithms, cyber threats, unauthorised access, lack of transparency, and more.
If you use AI tools, it’s important to check for these AI challenges and risks to be able to solve them. AI security governance is one such method that helps you identify those challenges and apply measures and policies to protect your AI deployments and data and keep them ethical, fair, and transparent.

In this article, we’ll explore how AI security governance improves your incident response plans to safeguard your AI systems from cyber threats and keep them compliant with laws and regulations.

Challenges and Risk with AI Solutions

Looking at the benefits of AI, more and more businesses and individuals globally are using AI tools and systems. But there are certain challenges with AI systems as well that you must understand before deploying them in your operations.

Algorithm Bias
Humans can have certain biases in terms of gender, race, religion, language, perception, etc. Created by humans, AI algorithms can inherit these biases. So, when you train AI systems with biased data, it generates biased results, which affects equality, fairness, and ethics. It can propagate discrimination in cases, such as lending, criminal justice, hiring, and so on. Algorithm biases can also provide many false positives or negatives. All of this offends and frustrates users and damages an AI system’s credibility.

This is why you must address algorithmic biases so that AI systems generate unbiased, fair outcomes. It requires you to test and remove biases during development and audit algorithms periodically.

Data Privacy
AI systems can raise data privacy risks as you need a massive amount of data to train them. This requires you to collect, store, and process information from various sources, which can include personal or sensitive data.

If you don’t have proper measures to protect and manage data, adversaries can breach data and launch cyberattacks. Failure to protect data leads could breach data protection regulations, such as GDPR, HIPAA, etc. You may face lawsuits and penalties, and lose customer trust. Solutions, such as AI security governance and using advanced security tools and technologies, can help you protect data and meet security compliance requirements.

Lack of Transparency
How an AI model makes decisions is a big question. It’s not easy to explain to people how they operate. This raises transparency issues in AI systems. But if you belong to highly regulated industries, such as healthcare, law, finance, defence, etc., transparency is needed to maintain trust of customers and stakeholders and comply with applicable laws and regulations. To increase AI algorithms’ interpretability, you need systems, such as AI governance and explainable AI (XAI).

Adversarial Attacks
Irrespective of how advanced your AI system is, it’s still vulnerable to cyber threats. The fact that AI systems are fed and trained on large data sets makes them a big target. Organisations with poor security controls and lack of cybersecurity awareness become an easy prey to data breaches, insider threats, phishing attacks, third-party attacks, and other threats.

Once attacked, the organisation may end up losing sensitive business and customer data, money in recovering from attacks and ransom payments, and getting their operations back to normalcy. Authorities and regulatory bodies increase scrutiny on them and may as well pose hefty fines due to non-compliance.

Integration Difficulty
Many organisations still use legacy systems even for critical business tasks. Lacking the modern technologies to scale, be flexible, or support heavy workloads, legacy systems may not be compatible with modern AI solutions. These compatibility issues may delay operations or create security loopholes. This is why it’s a good practice to not rush AI deployments. Use cloud services, APIs, etc. to bridge the gap between AI tools and legacy systems.

Other than the above challenges, an organisation may also face issues, such as a lack of AI skills, high implementation cost, and adoption resistance while deploying AI systems.

What Is AI Security Governance?

AI security governance means using processes and measures to secure AI deployments and data from cyber threats, and ensure they remain ethical and safe.

Organisations that use AI tools and solutions in their IT infrastructure must have a full picture of how these tools are developed, deployed, used, and maintained. AI security governance framework provides policies, frameworks, processes, and best practices to make sure AI is being used responsibly, ethically, fairly, and legally. It requires you to conduct real-time monitoring and address flaws, such as human errors and bias, in AI and ML algorithms that could introduce discrimination.

By mitigating these flaws and managing security and data governance in AI deployments, you can run responsible AI innovation in your organisation. It helps prevent data breaches, cyberattacks, human bias, and other errors and risks. This keeps you accountable for your actions and aligned with societal and organisational values. It also helps you make better business decisions, improve your security posture, motivate you to stay compliant with laws and regulations, and uphold customer and stakeholder trust.

Key Components of AI Security Governance

The AI security governance framework is based on some ethical and moral principles to ensure fairness, data security and privacy, and transparency, without bias. Here are the main components of artificial intelligence and machine learning security governance:

Risk management: Organisations need to detect and eliminate risks with AI deployments. You can conduct AI risk assessments to find unauthorised access to AI models, data theft, adversarial attacks, internal threats, etc. Risks could also be ethical, operational, or technical in AI systems.

Compliance: Complying with data privacy laws and regulations is essential in AI security governance. AI systems continue advancing, which is beneficial for users but also brings many challenges. Authorities and regulatory bodies, such as GDPR, ISO 27001, NIST, etc., enforce new standards to tackle those challenges and require you to comply to stay secure and avoid penalties.

Accountability: Organisations must stay responsible throughout the complete AI development or usage lifecycle. It requires you to set up clear roles and responsibilities and make informed decisions. Conduct audit trails periodically to check if your AI governance framework is effective or not, whether your efforts are paying off, and improve weak spots. It also allows you to trace actions and decisions back to people and for accountability.

Transparency: AI security governance promotes transparency between stakeholders as it gives complete oversight into AI systems and their decision-making process and results. This way, everyone can understand how a particular AI or ML algorithm works, the data sources they use, the resources they consume, and how they produce outcomes.

Ethics: Ethics is an important component of AI security governance. It requires you to check whether your AI system produces reliable and bias-free results. Techniques, such as fairness metrics and exploratory data analysis, help you find and remove biases in AI algorithms. This prevents algorithmic bias and discrimination, and maintains fairness for all groups and individuals.

How AI Security Governance Improves Incident Response Plans

Incident response planning in cybersecurity is a strategy to detect, assess, mitigate, and prevent security threats, vulnerabilities, and risks in your IT infrastructure. It helps you prepare for threats in advance, so you don’t get overwhelmed when a real attack strikes. It lets you create strategies for each stage of the attack lifecycle, so you can eliminate attacks confidently.

If you use AI systems in your operations, implement AI security governance to strengthen your incident response plans. It will help you solve the challenges that come with AI deployments and protect AI solutions and data from criminals. Incident response automation with AI governance also helps you keep your AI systems unbiased, ethical, safe, and transparent for users.

Let’s understand how you can improve your incident response plan with the AI governance framework:

Proactive Threat Detection
Attackers launch advanced cyber attacks on AI systems to breach data and disturb your workflows. This is the reason you need to detect threats before they convert into a full-blown cyber attack.

AI security governance requires you to continuously monitor your AI models and systems for cyber threats. This lets you detect AI security threats and vulnerabilities to be able to mitigate them while you still have time. As a result, you can reduce the impact of the threat on your business and even eliminate it altogether.

To detect threats, you can use automated vulnerability scanners, conduct penetration tests, or use an advanced intrusion detection and prevention system (IDS/IPS).

Risk Prioritisation
Security risks are of different types and pose different levels of impact on your business. Some risks are more critical than others in terms of system type, data sensitivity, exploitability, and more. If you give the same priority to all risks, you may miss out on handling critical risks. This may harm your business significantly while you were busy handling less-critical risks.

AI security governance requires you to safeguard your AI deployments with various security measures while maintaining transparency and ethics. You can use AI-based risk prioritisation to analyse large data volumes and identify risks that pose greater risks. It helps you prioritise risks based on severity, importance in business, and other factors at an impressive speed. This way, incident response teams can eliminate more critical risks first and safeguard vital assets and data from attackers.

Automated Incident Response
Traditional incident response requires you to manually analyse events, logs, alerts, etc. to detect abnormal incidents. The process is slower and prone to errors, and is limited to human capacity.

Automated incident response, on the other hand, identifies security incidents automatically by using modern technologies. It’s faster, less resource-consuming, and more accurate.

To improve the incident response, you can use automated tools and systems to find and remove threats continuously. For example, AI-based tools, such as IDS, automate the process of detecting security incidents. These tools quickly collect data from multiple sources, normalise it, and use ML algorithms and pattern detection to identify security incidents. As a result, you can respond to incidents faster and safeguard your systems and data.

Data Protection and Integrity
AI systems use massive data for learning. This data may include sensitive data and confidential business information. If an attacker becomes successful at perpetrating an attack on an AI tool, they can access this data. They can manipulate data to disrupt operations, sell this data on the dark web for money, expose business data to competitors or the public, or encrypt it to demand ransom.

Data protection is one of the principles of AI security governance. It requires all businesses using AI systems to protect sensitive data at all costs. While creating your AI incident response strategy, you can enforce strict data protection guidelines. You can use measures such as strong data encryption, multi-factor authentication (MFA), zero trust access, role-based access (RBAC), etc. to safeguard information and its integrity. This way, incident response automation will reduce the risk of insider threats, phishing attempts, and unauthorised data access.

Compliance Management
Authorities and regulatory bodies, such as GDPR, HIPAA, NIST, ISO 27001, etc., are set up to ensure organisations are using security measures to protect sensitive data from cyber threats. Looking at increasing attacks, their requirements have become stricter. Non-compliance can lead you to pay heavy fines and drag you to lengthy legal battles.

AI security governance in your incident response planning helps you meet compliance requirements. You can use AI tools to track compliance status with different regulations. It helps you find compliance gaps, so you can fulfill them to become compliant. It also helps you strengthen your security posture by finding and eliminating security risks and improving access controls. These controls help you avoid penalties and legal trouble while upholding customers' trust in your brand.

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How Microminder Improves Your AI Security Governance

Implementing AI security governance in your organisation offers you many benefits. It keeps your AI deployments safe and ethical, secure from threats, and compliant with laws and regulations.

If you’re looking for a reliable way to manage AI security, Microminder’s AI security governance is an excellent solution. It offers strong policies and security measures to protect your AI deployments and sensitive data from cyber threats. Here are some of the capabilities of AI security governance by Microminder:

Data protection measures, such as encryption and access controls, such as MFA, zero trust access, role-based access controls (RBAC), etc.
Guidelines to address AI deployment challenges, such as algorithmic bias, so that your AI systems generate fair results
Safeguards your AI systems from adversaries with vulnerability management, penetration testing, EDR, cloud security, security automation, and more

Keeps your AI systems compliant with compliance management services, GRC services, and more to adhere to standards, such as UK GDPR, NIST, ISO 27001, HIPAA, etc.

Book a call to explore Microminder’s AI Security Governance solutions