Are you asking yourself these AI governance questions?

 

Do we truly know where our sensitive data goes once it enters an AI model?

Without granular visibility into AI data flows, organizations risk “data leakage”, where proprietary secrets or personal identifiers are ingested by external models, creating irreversible privacy breaches. 

Are we still trying to manage AI risks using traditional IT security playbooks?

Standard cybersecurity frameworks often miss AI-specific vulnerabilities like prompt injection or data poisoning, leaving the organization exposed to sophisticated “blind-spot” attacks that traditional tools can’t see. 

Who is ultimately accountable for the decisions and actions taken by our autonomous agents?

A lack of formal governance leads to “rogue” AI deployments that can make biased, unverified or legally binding decisions without a clear human in the loop to provide oversight. 

Is our AI strategy flexible enough to survive the next wave of provincial and federal regulations?

With the rapid shift from voluntary guidelines to mandatory laws like Quebec’s Law 25, non-compliant AI systems now represent a significant financial and operational liability that could force a total system shutdown. 

Have we given our team a clear “Yes/No” list for using AI, or are they currently guessing at the safety of their prompts?

When employees lack specific security guardrails, they often default to “Shadow AI” tools that sit entirely outside the company’s protective perimeter, inadvertently sharing trade secrets with the public web. 

 

 

AI governance cyber

If you are, you’re not the only one.

 

Many organizations are inadequately prepared to secure their AI-driven initiatives, lacking both a cohesive cybersecurity strategy and the necessary technical capabilities to defend against AI-augmented threats. 

The advent of agentic AI presents an even greater risk: autonomous AI agents do not simply process data; they make decisions, trigger actions and operate across systems with minimal human oversight. 

AI-driven initiatives

CGI AI Governance, Risk and Compliance: Because adopting generative and agentic AI responsibly is your only option

CGI’s AI GRC offering combines six services to help CISOs, CDAOs and AI leaders build the structures, policies and processes needed to govern AI responsibly end-to-end, enabling trusted, scalable AI adoption while aligning with each organization’s sector-specific regulatory requirements.

AI Security Awareness Training 

Equip employees and leadership with the knowledge required to use AI responsibly 

AIMS Internal Auditing (ISO 42001) 

Prepare organizations for ISO/IEC 42001 certification and strong AI governance 

AI Threat, Risk & Privacy Impact Assessments 

Provide a structured evaluation of security and privacy risks introduced by AI 

AI Security Advisory & Governance 

Establish strong governance structures and responsible AI practices across the organization 

AI Regulatory & Compliance Consultation 

Ensure AI initiatives comply with emerging global regulatory frameworks 

AI Risk Management Framework Development 

Develop structured approaches to identifying and managing AI risk 

Knowing emerging threats helps you focus on the right guardrails

 

AI GRC

 

Data misuse, algorithmic bias, uncontrolled model drift and regulatory violations are no longer hypothetical risks—they are active fault lines running beneath every AI deployment. There are already numerous accounts of how AI has proven to be a liability for organizations.

Many events have resulted in serious damage, such as intellectual proprietary data exposure, compromised business operations, organizational data deletion or even a total shutdown of client-facing services.

The Open Worldwide Application Security Project (OWASP) has been documenting the evolution of AI vulnerabilities through Top 10 lists, while the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO) have developed standards to guide digital leaders through the growing risk intricacies.

 

Begin your journey today

NIST mentions risks to the Value Chain and Component Integration

Automated AI workflows risk the invisible integration of untraceable third-party parts and unverified, poorly sourced data.

OWASP warns of Prompt Injection in LLM applications for instance

Attackers feeding malicious instructions to LLMs is still the most common way to bypass safeguards and exfiltrate data.

OWASP also highlights Human-Agent Trust Exploitation with Agentic AI use

Malicious actors are exploiting agents’ perceived “authority” to manipulate humans into approving harmful actions.