AI Governance: What Tech and Security Professionals Need to Know
The rise of artificial intelligence (AI)—encompassing generative AI, agentic AI, and automation—has begun to reshape various business sectors, particularly cybersecurity. A recent study conducted by the Cloud Security Alliance revealed that nine out of ten organizations are either currently adopting or planning to adopt generative AI for security purposes.
Moreover, the same study, which surveyed 2,486 IT and security professionals, indicated that 63% believe in AI’s potential to enhance security, specifically in threat detection and response capabilities.
The Growing Concern of AI Usage
As enterprises, including small businesses, experiment with AI to evaluate its benefits, concerns are surfacing regarding the ethical implications and potential misuse of these technologies. Organizations are facing challenges related to shadow AI usage among employees, where unauthorized AI tools are being implemented, and cybercriminals are leveraging AI for malicious purposes.
This landscape has sparked a heightened interest in AI governance, which addresses critical issues such as algorithmic biases, security breaches, and ethical concerns surrounding AI technologies. Regulatory bodies in the U.S. and EU are also scrutinizing the need for stricter regulations, as highlighted in a survey by Markets & Markets.
Market Projections for AI Governance
The AI governance market, valued at approximately $890 million in 2024, is projected to skyrocket to $5.8 billion within four years, showcasing an annual growth rate of about 45%. Organizations must confront these issues proactively; otherwise, their security and privacy teams will be overwhelmed with challenging questions.
As stated by industry experts, AI governance is transitioning from a theoretical concept to a pressing operational reality. The RSA Conference in April underscored this urgency, emphasizing that the question for security teams is not if AI introduces risks, but how quickly they can adapt their frameworks to manage these risks without hindering innovation.
Skills Required for AI Governance
As the AI governance market experiences significant growth, there is an increasing demand for security professionals who are knowledgeable about AI platforms and versed in privacy regulations and cybersecurity issues related to this evolving field.
Despite the emergence of new AI models, the governance surrounding these technologies remains in its infancy. Current regulations do not adequately address safety, security, and data privacy concerns, necessitating the development of frameworks and safeguards.
Building Ethical and Compliant AI Systems
At recent conferences, professionals from tech and security sectors have been grappling with constructing ethical, compliant AI systems that maintain user trust while minimizing organizational risk. This endeavor often feels akin to building a plane while flying it.
Experts recommend treating AI governance with the same rigor applied to critical security domains: ensuring visibility, implementing automation, and establishing repeatable processes. This involves understanding which models are being utilized, the flow of data, access controls, and decision-making processes.
Collaboration and Governance Frameworks
Security professionals are now focusing on developing frameworks and playbooks to fortify AI governance. Privacy foundations like the Record of Processing Activities (RoPAs) and Data Protection Impact Assessments (DPIAs) are becoming increasingly valuable resources.
As articulated by industry leaders, effective governance doesn’t have to be perfect but must be proactive, transparent, and integrated into the core of AI development and deployment.
The Role of AI Bill of Materials (AIBOM)
There is a call for organizations to adopt an AI Bill of Materials (AIBOM) and other protective measures to track the code used in AI tools. Ensuring trust in software for the increasingly regulated, AI-driven environment requires maturing AI governance to include AIBOM, detecting copyright violations in AI code suggestions, redacting sensitive data during analytics, and improving techniques for testing the relevance and security of AI models.
AI Governance Across Disciplines
AI governance draws parallels to the early days of cloud computing, where rapid adoption led to privacy, security, and regulatory challenges. Organizations are encouraged to adopt a shared security model for AI governance, addressing potential risks proactively.
For businesses utilizing third-party AI tools, recognizing the introduction of a shared security responsibility model is crucial. This necessitates visibility into vendor infrastructures, data handling practices, and model behavior to mitigate risks effectively.
Upskilling for the Future
To ensure effective AI governance, a skilled workforce that facilitates cross-functional collaboration is essential. This includes security, privacy, legal, HR, compliance, and data leaders. Cybersecurity professionals must also engage in industry collaboration, sharing successful governance models and insights to establish standards for securing AI.
The need for security practitioners to upskill in AI technologies and data governance is paramount. Understanding system architectures, communication pathways, and agent behaviors will be vital in managing risks associated with AI.
As organizations adapt to AI, security teams must recognize that without well-constructed guardrails, AI can inadvertently access personal information, potentially compromising privacy and rights. Moreover, AI used in advanced cybersecurity measures can be exploited if not properly governed.
To prepare for current and future AI challenges, organizations should integrate privacy professionals into their security planning. Contrary to the misconception that privacy concerns inhibit security efforts, establishing robust privacy foundations is essential for a comprehensive security strategy.
