Models, data, and responsible controls
Securing AI Toronto
Assess AI and machine learning risks across training, deployment, usage, and governance, with practical controls for your environment.
Built around your environment
Securing AI Systems, Training Data, and Decision Processes
AI systems face risks beyond conventional infrastructure security, including adversarial inputs, poisoned training data, model theft, sensitive-data exposure, and misuse. Models, training pipelines, deployment systems, and decision-making processes all require security review.
WEBSITETOON's Securing AI service assesses artificial intelligence and machine learning systems and supports hardening, LLM safeguards, responsible development, governance, and incident planning. Adversarial testing and AI red teaming take place only with explicit authorization, defined targets, approved techniques, and agreed data-handling boundaries.
Serving Richmond Hill, Toronto, and the GTA, we scope work for proprietary models, open-source models, and third-party AI tools. Assessment, monitoring, incident response, and governance arrangements are agreed for each engagement; they do not guarantee model safety, uninterrupted operations, or automatic regulatory compliance.
Our services
Choose the right starting point.
Scope the services around your systems, data, business priorities, and review requirements.
AI Security Assessments
Evaluate AI vulnerabilities, attack surfaces, models, training pipelines, deployment infrastructure, and access controls. Model integrity checks and prompt injection detection can support the assessment within tool and scope limitations.
Adversarial Robustness Testing
With explicit authorization, test evasion, poisoning, and model extraction scenarios in approved environments. Document observed robustness limits and provide hardening recommendations rather than guaranteeing resilience against every input.
AI Red Teaming
Run explicitly authorized attempts to manipulate models, extract permitted test information, and bypass safety controls within agreed rules of engagement. Sensitive-data access, model extraction, and destructive techniques require specific approval.
LLM Security and Prompt Engineering
Assess Large Language Models (LLMs) for prompt injection, jailbreaking, and misuse. Implement and evaluate guardrails, content filtering, prompt design, and safety layers to reduce harmful outputs, without promising that all bypasses are prevented.
AI Governance and Compliance Support
Develop governance frameworks, responsible AI policies, bias detection, fairness testing, audit trails, and control documentation. This work supports regulatory readiness and relevant controls; legal applicability and compliance require separate review.
Secure AI Development
Work with development teams on secure training pipelines, training-data handling, secure coding for machine learning, model versioning, access controls, fine-tuning practices, and deployment best practices.
AI Incident Response
Scope containment, damage and data-exposure assessment, recovery planning, and post-incident analysis for compromised AI systems. Response coverage and schedules are agreed in the engagement, with recovery dependent on the incident and available resources.
Third-Party AI Usage Controls
Review ChatGPT and other AI-tool usage for sensitive-data leakage, prompt injection, access, and policy risks. Support secure usage policies, guardrails, and employee training on safe AI use.
A considered approach
Understand the details before you begin.
Review the capabilities, requirements, and limitations relevant to your engagement.
AI Security Threat Categories
Assess relevant threats against your actual models, data, access paths, and integrations. Coverage and authorized test techniques are selected for the engagement.
- Adversarial attacks: crafted inputs intended to cause incorrect predictions or other unintended behavior
- Data poisoning: malicious training data that changes model behavior
- Model extraction or theft: attempts to reproduce trained models through API queries or other access paths
- Model inversion: attempts to reconstruct sensitive training information from outputs
- Prompt injection: crafted LLM inputs intended to redirect behavior or bypass safeguards
- AI supply chain attacks: compromised pre-trained models, libraries, or dependencies
- Bias exploitation: malicious manipulation of biased model behavior
- AI-powered attacks: attackers using AI to enhance attacks against systems or users
How AI Security Differs from Traditional Cybersecurity
Alongside systems, networks, and data, AI security considers model intellectual property, memorized or leaked training information, adversarial robustness, and the consequences of automated decisions. Manipulated predictions can create safety risks in settings such as autonomous systems. Biased or misused outputs can create discrimination, reputational, and legal risks; security controls alone do not resolve every ethical issue.
Open-Source and Proprietary Model Security
Review Llama, Mistral, Stable Diffusion, and other open-source or proprietary models for supply chain risks, model integrity, secure fine-tuning, access controls, and deployment practices. Controls should reflect the model source, license, data sensitivity, and operating environment rather than assuming any model is inherently secure.
AI Governance and Regulatory Context
Review applicable Canadian privacy and sector-specific obligations and relevant EU AI Act requirements with legal advisers. Canada's Artificial Intelligence and Data Act (AIDA) is a legislative-policy reference whose current status and applicability must be checked, not assumed to be enacted or in force. Governance work supports risk assessments, mitigation, transparency, and control evidence but does not establish automatic compliance with current or emerging rules.
Related Security Services
Broader Cybersecurity, Penetration Testing, and Ethical Hacking services can complement Securing AI by assessing authorized infrastructure, application, and integration risks.
Our process
A clear path from planning to review.
Agree on priorities, responsibilities, and measures before implementation.
- 01
Development
Agree system boundaries, authorized access, objectives, and engagement schedules. Review secure training-data handling, model development practices, secure coding for ML, training pipelines, versioning, and fine-tuning controls.
- 02
Testing
Perform explicitly authorized adversarial and red team testing, bias detection, fairness evaluation, and robustness checks. Document permitted methods, evidence, limitations, and hardening recommendations.
- 03
Deployment
Review secure model serving, access controls, deployment configuration, monitoring, and logging. Validate agreed guardrails and safety layers against relevant scenarios rather than assuming they prevent every harmful output.
- 04
Operations
Scope recurring or continuous monitoring, drift detection, model updates, and incident response. Monitoring coverage, alert channels, response schedules, and approved recovery actions are agreed, without an implied round-the-clock service or guaranteed recovery outcome.
- 05
Governance
Develop policies, control and compliance monitoring, audit trails, and documentation. Review responsible AI practices and regulatory applicability with relevant stakeholders and legal advisers; controls do not confer automatic compliance.
Frequently asked questions
Start with the right questions.
Clarify scope, expectations, and responsibilities before choosing your next step.
Do we need AI security if we only use ChatGPT or other AI tools?
Third-party AI tools can introduce sensitive-data leakage, prompt injection, misuse, and policy or compliance risks even when you do not train a model. Scoped support can include usage policies, guardrails, employee training, and review of data and access practices.
How much does AI security cost?
Request a scoped quote based on the number and type of AI systems, models, data sensitivity, deployment architecture, authorized testing, governance needs, and monitoring or incident support. Deliverables, testing schedules, and response arrangements are agreed in the engagement.
Is AI security necessary if our infrastructure is already secured?
Infrastructure controls remain important, but they may not address model manipulation, poisoning, extraction, training-data leakage, prompt injection, or harmful use of outputs. AI-specific review identifies relevant risks and controls; no model or assessment can be assumed to be secure against every attack.
What regulations apply to AI in Canada?
Applicable obligations depend on the organization, sector, data, use case, and markets served. Review Canadian privacy and sector-specific requirements and relevant EU AI Act obligations with legal advisers. Check the current legislative status of Canada's Artificial Intelligence and Data Act (AIDA) rather than treating it as an in-force requirement. Our work supports governance and controls, not a legal determination or automatic compliance.
Can you assess AI built on open-source models?
Yes, work can be scoped for models such as Llama, Mistral, and Stable Diffusion, as well as proprietary AI. Review supply chain security, model integrity, secure fine-tuning, access controls, and deployment practices. Any adversarial testing requires explicit authorization and defined scope, and results do not guarantee complete protection.
Plan Security for Your AI Systems
Share your models, AI tools, data flows, deployment environment, and governance priorities with WEBSITETOON to scope an AI security assessment.