AI is now a part of applications and business processes, as well as customers' experiences, analytical platforms and crucial decision-making workflows. As companies adopt the use of machine-learning, LLMs, Generative AI, RAG systems, AI agents, as well as AI-powered applications Security and governance have to adapt with them.
What We Help Secure and Govern
ZechionAI assists organizations in assessing AI security risks, secure AI systems implement a practical approach to governance, enhance accountable AI practices, plan for the applicable compliance requirements and control AI security throughout every stage of AI lifecycle.
Starting with AI security design and architecture, as well as ML security LLM red teaming AI privacy model governance and vendors' risk and audit-readyness as well as managed AI security. We integrate technological controls to business and risk-related requirements.
Our method:
- Assess
- Secure
- Govern
- Test
- Monitor
- Validate
- Improve
- Operate
- Machine-learning and AI systems
- LLM Applications for Generative AI and LLM
- Knowledge systems for enterprise and RAG
- AI agents and workflows for tool-calling
- AI APIs as well as integrations
- Models and infrastructure for model-serving
- Pipelines for training and inference
- AI data and data
- Artificial Intelligence and Cloud infrastructure
- Credentials, identities access, and identities
- AI vendors and services from third parties
- AI process of development and deployment
AI Security, Governance & Compliance services
14 services
AI Security Is More Than Cybersecurity
Traditional cybersecurity remains vital however, AI introduces new considerations about models training data and prompts, as well as inference behaviour, model outputs, AI agents, evaluation of human oversight and lifecycle governance.
A successful AI security program could be able to combine:
- Cybersecurity
- Security of applications
- Cloud security
- Security of data
- Privacy
- Access management and identity management
- Model security
- AI-specific threat modeling
- AI red teaming
- AI Governance
- Responsible AI
- Model risk management
- Vendor risk management
- Monitoring and the ability to observe
- Human supervision
- Audit and management of evidence
The best combination is determined by the company's AI usage cases, design and data sensitivity, models risk infrastructure, exposure to regulatory maturity, the vendor ecosystem and the business requirements.
The NIST AI Risk Management Framework is similar to the AI Risk Management Framework. It is a risk-based framework that allows for flexibility in implementation, rather than an all-encompassing checklist, with AI risk management being incorporated into every stage of AI lifecycle.
What Is AI Security, Governance & Compliance?
AI Security
- AI applications
- Models
- LLMs
- AI agents
- APIs
- Data
- Infrastructure
- ML pipelines
- Credentials
- Deployment environments
- Third-party AI services
AI security focuses on securing the AI technology and operational environment around AI.
This may comprise:
The goal is to detect and mitigate security risks such as unauthorised access, leakage of information and modeling abuse, prompt injection and theft of models poisoning, unsecure integrations, and any other risks that are appropriate for the system.
AI Governance
AI governance establishes how an organization makes decisions about AI.
It could be defined as:
- Who is the owner of AI risk
- What systems need to be reviewed?
- What AI uses are classified
- What kind of documentation is needed?
- What controls are in effect?
- If human approval is required
- The way AI-related systems are supervised
- How are changes accepted
- The time when systems need to be reviewed or removed
AI Compliance
AI compliance is based on identifying and addressing relevant:
- Laws
- Regulations
- Industry needs
- Contractual obligations
- Customer expectations
- Internal policies
- Security frameworks
- Privacy regulations
The term "compliance readiness" is not the same as legal approval or approval from the regulatory authorities.
ZechionAI can assist organizations in identifying controls that are required, evaluate weaknesses, document controls, gather evidence and prepare for external or internal review. Legal interpretation must be handled by an experienced lawyer when needed.
Why AI Security & Governance Matters
AI can cause security and governance issues that are hard to assess from a traditional security-focused perspective.
Companies may have to deal with:
- Sensitive information is exposed via outputs or prompts
- Unregulated employee use of AI services
- Shadow AI
- Access controls of the weak model
- Insecure AI APIs
- Prompt injection
- Jailbreak attempts
- Training-data risks
- Data poisoning
- Model extraction
- Model theft
- Inadequate model documentation
- Poor risk classification
- Unknown AI ownership
- Weak human oversight
- Third-party model dependencies
- Inadequate surveillance
- AI supply-chain risks
- Uncertainty of subprocessors and vendors
- Governance isn't perfect.
- Uncertainty in the regulatory system
There isn't a single individual security item.
A well-established AI security software connects policy, people technology, data identity infrastructure and governance, as well as testing and monitoring.
AI Security Strategy & Consulting
Build an AI Security Program Around Your Actual Risk
- AI Security Strategy
- AI Security Roadmap
- AI Security Maturity Assessment
- AI Security Readiness Assessment
- AI Threat Assessment
- AI Attack-Surface Assessment
- AI Security Architecture Consulting
- AI Security Operating Model
- AI Security Policy Development
- AI Security Transformation Planning
- AI Security Build-vs-Buy Assessment
- Enterprise AI Security Advisory
ZechionAI assists organizations in establishing AI security plans that reflect their technology environment instead of implementing controls just because they're available.
Our AI security consultations can comprise:
We examine the connections between:
- AI applications
- Models
- Data
- APIs
- Users
- Identities
- Cloud infrastructure
- ML pipelines
- Vendors
- Security controls
- Monitoring
- Governance processes
A Risk-Proportionate Approach
The goal is not to include security controls just because they are there.
The goal is to establish control measures that are proportional to:
- Impact on business
- AI use the case
- Sensitivity of data
- Model risk
- Threat environment
- Exposure to regulatory risk
- Infrastructure
- Users
- The operational requirements
- Maturity of the organization
AI Risk Management & Governance
Turn AI Risk Into an Operating Discipline
- AI Risk Management Strategy
- AI Governance Framework
- AI Risk Assessments
- AI Use-Case Classification
- AI System Inventories
- AI Risk Registers
- AI Governance Operating Models
- AI Policies & Procedures
- AI Lifecycle Governance
- AI Approval Workflows
- AI Change Management
- AI Risk Monitoring
- AI Governance Dashboards
- AI Governance Automation
AI governance is best utilized when it is integrated into the way an organization operates, not as a file that is kept in the compliance folder.
ZechionAI can help establish:
AI Risk Categories
Based on the purpose Risk assessments can take into consideration:
- Security Risk
- Privacy risk
- Model risk
- Data risk
- Operational risk
- Vendor risk
- Risks for business
- Risk of reputation
- Human-impact risk
Governance Lifecycle
- Discover
- Classify
- Assess
- Approve
- Deploy
- Monitor
- Review
- Retire
This is in line with the general concept of AI risk management must be continuous and iterative instead of a one-time evaluation.
Responsible AI & Ethical AI
Build AI That Can Be Evaluated, Explained and Governed
- Responsible AI Strategy
- AI Ethics Frameworks
- Fairness Assessments
- Bias Assessment
- Bias Monitoring
- Explainability
- Transparency
- Human Oversight
- Human-in-the-Loop Controls
- AI Impact Assessments
- Responsible AI Documentation
- Model Cards
- Risk Documentation
- Ethical AI Governance
Responsible AI is not about asserting the fact that any AI system is completely honest or impartial.
It's about creating procedures to identify, assess the severity, mitigating, recording the risks that are relevant, and then monitoring them.
ZechionAI supports:
The amount of fairness, justification as well as transparency and human oversight is determined by the case of use, the decision context, impact potential on the model, industry, requirements, and risk tolerance.
NIST recognizes reliable AI traits, such as security and resilience as well as transparency and accountability. the ability to explain and interpret Privacy enhancement, as well as control of harmful bias.
Responsible AI is an ongoing process and not an annual certification exercise.
AI Regulatory Compliance & Readiness
Prepare for Applicable AI Requirements With a Risk-Based Approach
- Industry
- Jurisdiction
- Use for case
- Data
- Customers
- Contracts
- Business activities
- AI system attributes
- Risks to the regulatory system
AI requirements can be very different dependent on:
ZechionAI is able to support:
- AI Compliance Strategy
- AI Compliance Readiness Assessments
- AI Compliance Gap Analysis
- AI Regulatory Risk Assessments
- AI Documentation
- AI Control Mapping
- Audit Readiness
- Evidence Management
- Compliance Reporting
- Regulatory Monitoring
- Industry-Specific Compliance Readiness
- Cross-Jurisdiction AI Governance
We can assist organizations in mapping pertinent requirements to controls and processes, policies Evidence, and the right to.
What We Do Not Claim
ZechionAI is not a guarantee that an assessment is automatically able to provide:
- Legal conformity
- Government approval
- Authorization from the regulatory authorities
- Legal representation
- Official accreditation
- Guaranteed certification
Any applicable regulations should be reviewed by qualified legal or regulatory experts when appropriate.
AI Security Architecture & Engineering
Security by Design, Not Security After Deployment
- Enterprise AI Security Architecture
- ML Security Architecture
- LLM Security Architecture
- Generative AI Security Architecture
- AI Agent Security Architecture
- AI API Security
- AI Data Security
- AI Infrastructure Security
- AI Identity Architecture
- Zero-Trust AI Architecture
- Secure AI Platform Architecture
- Hybrid AI Security
- Cloud AI Security
AI security architecture must be considered before any sensitive AI systems are put into production.
ZechionAI can engineer and design:
The architectural considerations may comprise:
- Authentication
- Authorization
- Identity
- Protection of data
- Encryption
- Secrets management
- API security
- Network control
- Model protection
- Logging
- Monitoring
- Threat detection
- Security automation
The aim is to incorporate security an integral part within the AI architecture, instead of being entirely dependent on security controls that were that are added after deployment.
ML, LLM & Generative AI Security
Machine Learning Security
- Models of threat ML
- Training pipeline security
- Model security
- Data poisoning risks
- Model extraction risks
- Model theft risk
- Adversarial attacks
- Dependency security
- ML supply-chain security
- Model artifact protection
- Model registry security
ZechionAI is able to assess and enhance:
Secure the Models Behind Modern AI Applications
LLM Security
LLM environments might require some controls in relation to:
- Prompt injection
- Jailbreak attempts
- Sensitive-data leakage
- Unsafe outputs
- Model abuse
- LLM APIs
- Provider dependencies
- Fast handling
- Context security
- Tool use
- Output validation
Generative AI Security
We're in support of:
- GenAI risk assessments
- GenAI application security
- Generative AI protection of data
- Controls for security of content
- AI prevention of abuse
- Model access controls
- AI security monitoring
- GenAI vendor assessments
- GenAI Governance
There is no single method that can completely remove every LLM and Generative AI danger. For effective security typically requires multiple layers control testing, monitoring, governance, and supervision.
The NIST Generative AI Profile specifically addresses the risks that are unique to or increased by Generative AI and offers suggestions for measures to reduce risk across every stage of AI lifecycle.
AI Data, Privacy & Identity Security
Protect the Data and Identities Surrounding AI
- AI Data Security
- AI Privacy Assessments
- AI Data Governance
- Sensitive Data Discovery
- PII Detection
- Data Classification
- Data Access Controls
- Data Loss Prevention
- Encryption
- Data Masking
- Tokenization
- Training Data Security
- Dataset Governance
- Data Lineage
- Data Provenance
- AI Identity Management
- Role-Based Access Control
- Attribute-Based Access Control
- Machine Identity
- Service Identity
- Agent Identity
- Secrets Management
Secure the model is only part of the challenge.
AI systems also rely on documents, datasets APIs, users identity and credentials for service providers and other connected applications.
ZechionAI can assist in:
Identity Matters
AI applications can use databases Enterprise systems APIs, files, knowledge bases and other external tools.
This makes authorization and identity essential aspects in AI security.
Access should be determined by:
- Who is the person requesting access
- What system is it that's requesting it?
- What kind of data are involved?
- What is the request for action?
- What authorizations are suitable?
- If additional approval is needed
AI Red Teaming, Testing & Threat Detection
AI Red Teaming
- AI red teaming
- LLM red teaming
- GenAI red teaming
- Red teaming of AI agents
- Testing of adversaries
- Prompt injection testing
- Jailbreak testing
- Model extraction testing
- Assessment of data poisoning
- Testing for Evasion
Services could include:
Test AI Systems Before Attackers Test Them
AI security testing must be consistent with the current structure and the model of threat.
AI Security Testing
- AI application testing
- Model security testing
- API security testing
- AI vulnerability assessments
- AI penetration testing
- Security regression testing
- The pre-production AI security testing
AI Threat Detection
- AI threat monitoring
- Model abuse detection
- AI API abuse detection
- Data-exfiltration detection
- Prompt-attack detection
- Monitoring of the behavior of agents
- AI anomaly detection
- Security alerting
Testing can be done using specific attack scenarios and business-impact scenarios instead of using general vulnerability checklists.
AI Audit, Compliance Automation & Vendor Risk
AI Audit
- AI governance audits
- AI security audits
- AI Control assessments
- AI compliance gaps assessments
- AI evidence collection
- AI audit documentation
- AI risk reporting
ZechionAI supports:
Make AI Controls Easier to Demonstrate and Maintain
Compliance Automation
The potential capabilities could include:
- AI control automation
- Evidence automation
- Monitoring of compliance
- Enforcement of the policy
- Governance dashboards
- Risk dashboards
- Automated AI inventories
- Model-documentation workflows
AI Vendor Risk
Third-party AI providers can become part of your company's threat surface and governance system.
Assessments of Vendors may include:
- Data handling
- Security controls
- Privacy
- Model behavior
- Availability
- Dependencies
- Subprocessors
- Data retention
- Controls for contracts
- Incident response
- Exit strategy
Services may include:
- AI Vendor Risk Assessments
- LLM Provider Risk Assessments
- AI SaaS Assessments
- Third-Party Model Assessments
- AI Supplier Due Diligence
- AI Data-Processing Risk Assessments
- Contract-Risk Support
- Third-Party AI Monitoring
Managed AI Security, Governance & Compliance
Keep AI Risk Management Active as Your Environment Changes
- Managed AI Security
- Managed AI Governance
- Managed AI Risk Monitoring
- Managed AI Compliance Monitoring
- Managed AI Security Monitoring
- Model Governance
- Vendor Risk Monitoring
- AI Security Advisory
- AI Governance Reporting
- AI Incident Response Support
- Continuous Risk Assessment
- AI Policy Maintenance
- AI Security Optimization
AI security is not always thought of as a single-time initiative.
Models change. Vendors change. Applications develop. The latest AI agents are created. New sources of data are accessible. Threat techniques develop.
ZechionAI provides ongoing support for:
Flexible Engagement Models
- Project-based
- Assessment
- Retainer
- Managed service
- Fragmental AI security team
- A dedicated AI security team
Generative AI, RAG & AI Agent Security
LLM Security
- Prompt injection
- Jailbreaks
- Sensitive-data leakage
- Model-provider risks
- API security
- Controls for output
Controls can refer to:
Security for AI Systems That Retrieve, Reason and Act
Modern AI applications typically expand beyond a single model.
They can retrieve information from the enterprise or call APIs, utilize tools, perform workflows or interface with systems for business.
This alters it's security model.
RAG Security
ZechionAI can evaluate:
- Knowledge-base security
- Retrieval authorization
- Vector database security
- Protection for embedded devices
- Controls for access to documents
- Retrieval poisoning
- Data leakage
- Source Governance
AI Agent Security
AI agents may add additional security issues when they retrieve information, use tools to call to use access devices, or carry out actions.
Security concerns may include:
- Agent's identity
- Permissions for tools
- Action authorization
- Agent isolation
- Human approval
- Tool-calling security
- Monitoring of the behavior of agents
- Agent audit trails
The greater the authorities an AI system is granted access to information or execute tasks, the more vital identification, authorization monitoring, human approval controls become.
AI Security & Governance Across the AI Lifecycle
AI security is expected to follow the AI security process from the beginning to the time of retirement.
- Plan
- Design
- Develop
- Train
- Test
- Approve
- Deploy
- Monitor
- Review
- Update
- Retire
- 01
Plan
- Classification of use-cases
- Risk assessment
- Assessment of data
- 02
Design
- Threat modeling
- Security architecture
- Governance design
- 03
Develop
- Secure development
- Dependency management
- Controls for access
- 04
Train
- Dataset governance
- Protection of data
- Training security
- 05
Test
- Security testing
- Testing for Robustness
- AI assessment is the responsibility of the responsible AI assessment
- 06
Approve
- Governance review
- Risk acceptance
- Documentation
- 07
Deploy
- Secure infrastructure
- Controls of access
- Monitoring
- 08
Monitor
- Security-related occasions
- Model behavior
- Drift
- Abuse
- 09
Review
- Assessment of risk over time
- Control validation
- Vendor review
- 10
Retire
- Revocation of access
- Data retention
- Model archival
- Documentation
Industries We Serve
All industriesZechionAI adjusts AI security as well as governance according to the company's use cases, risk profile data, infrastructure as well as the operating system.
Healthcare & Life SciencesClinical AI security; Patient-data protection; Healthcare AI governance; Risk assessment for medical AI; AI for medical imaging security; Healthcare NLP security; Generative AI Governance; Clinical decision-support oversight; Privacy of AI; Model governance; Vendor risk
Banking & Financial ServicesFraud-detection security; Credit-risk model governance; Financial AI risk management; Algorithmic decision-making governance; Transaction-monitoring AI; Model-risk controls; LLM security; Protection of customer data; AI vendor assessments; Model validation; AI audit readiness Insurance AI underwriting governance; Claims AI security; Risk-model governance; Fraud-detection governance; Security of customer-related data; Algorithmic fairness; Model documentation; Model monitoring; AI risks from third-party AI
Retail & E-CommerceRecommendation governance; Personalization security; Security of customer-related data; AI fraud prevention; Dynamic-pricing Governance; Generative AI security; AI chatbot security; AI vendor risk; Model monitoring
Technology, SaaS & Digital PlatformsAI SaaS security; ML platform security; LLM security; AI API security; AI application security; AI agent security; Model protection; AI supply chain security; Secure AI development; AI Governance; AI security testing
Transportation, Mobility & AutomotiveGovernance of AI in the Automotive; Fleet AI security; Predictive-maintenance governance; Route optimization security; Forecasting demand; Driver analytics; Computer vision security; AI in real-time security; Model monitoring- Manufacturing & IndustrialIndustrial AI security; Predictive-maintenance governance; Computer vision security; Quality-control AI; AI and robotics governance; IoT/AI security; Industrial anomaly detection; Edge AI security; Monitoring of AI production
- Energy & UtilitiesPredictive-maintenance AI; Energy forecasting; Asset-monitoring AI; Industrial AI security; Infrastructure security; Anomaly detection; AI monitoring; Model governance; AI risks from third-party AI
Real Estate & PropTechModels for valuing properties; Pricing algorithms; Tenant analytics; Lead-scoring AI; Property-management AI; Predictive maintenance; Document intelligence; Generative AI security; Privacy of AI; Algorithmic fairness- Government & Public SectorGovernance of AI in the Government; Public-sector AI risk management; Citizen-service AI security; Document-intelligence governance; Fraud detection; Anomaly detection; AI Transparency; Human supervision; AI model governance; Responsible AI; Audit readiness ZechionAI does not mean the approval of the government, eligibility for procurement certification, or authorization unless specifically stated.
Education & EdTechProtection of student data; AI tutor security; Personalized-learning governance; Learning analytics; Generative AI Governance; AI assessment security; Privacy of AI; Responsible AI
Media, Entertainment & AdvertisingRecommendation governance; Content generation security; Generative AI Governance; Content-moderation AI; Audience analytics; Personalization; Optimization of advertising; LLM security; AI data management Telecommunications Network anomaly detection; Fraud detection; Customer analytics; Predictive maintenance; Optimization of the network; AI infrastructure security; In real-time AI monitoring; LLM security; AI API security
Professional & Business ServicesIn addition, there are accounting, legal and HR. corporate and recruitment. Document intelligence security; Contract AI Governance; Legal AI risk assessment; Recruitment AI Governance; Fairness in candidate-matching; Workforce analytics; AI assistants; Generative AI security; Protection of sensitive data; Vendor assessment- Cybersecurity & Risk TechnologyML security; AI threat detection; AI red teaming; Testing ML for Adversarials; LLM security; AI agent security; Model protection; AI supply chain security; Threat modeling; AI incident response planning
Food, Beverage & HospitalityForecasting demand; Customer-specific personalization; Recommendation Systems; Revenue optimization; Workforce analytics; Fraud detection; Customer-service AI; AI chatbot security; Protection of customer data
Construction & Facility ManagementPredictive maintenance; Computer vision; Site-safety AI; Monitoring of equipment; Workforce analytics; Forecasting for projects; IoT/AI security; Model governance
Pharmaceutical, Biotechnology & ResearchDrug-discovery AI; Research-model governance; Security of data from scientific research; AI model validation; Dataset governance; Model lineage; Research AI security; Generative AI Governance; Responsible AI; AI threat from third party
Industry-Specific AI Security & Governance
There isn't a one-size fits all AI security technology.
A healthcare AI system processing sensitive patient data may require a completely different control system than the internal assistant to marketing. A healthcare AI agent that has an access point to the financial services could require greater approval and authorization controls than the only read analytics model.
AI Security & Governance Technology Capabilities
AI & ML Security
- ML security
- Model security
- Model registries
- ML pipelines
- Testing for adversarial purposes
- Model monitoring
GenAI & LLM
- LLM APIs
- Prompt security
- LLM monitoring
- LLM evaluation of the LLM
- RAG security
- Embedding security
- AI agent security
Infrastructure
- Cloud security
- Containers
- Kubernetes
- Infrastructure security
- Secrets management
- Management of identities
Data
- Classification of data
- Protection of data
- Data lineage
- Data source
- Data loss prevention
- Privacy controls
Security Operations
- Logging
- Metrics
- Threat detection
- Alerting
- Security monitoring
- Incident response
Technology-related recommendations are vendor neutral and should reflect the business's current environment, architecture financial plan, tolerance to risk and capabilities for operation.
AI Security, Governance & Reliability
Governance, security and reliability are interrelated, but they each solve a different problem.
Security
- Authentication
- Authorization
- Encryption
- Secrets management
- Control of access
- Network control
- Dependency management
- Model security
Governance
- AI inventory
- Model inventory
- Risk classification
- Documentation
- Versioning
- Approval workflows
- Lineage
- Audit trail
- Lifecycle management
Reliability
- Health checks
- Failure handling
- Rate limiting
- Monitoring
- Alerting
- Incident response
- Recovery plan
- Dependency management
Controls should be chosen according to the structure and risk, failure modes business impact, operational needs.
Business Outcomes
A well-established AI security and governance programme can assist businesses:
- Reduce the unmanaged AI risk
- Increase the visibility of AI systems
- Be sure to protect sensitive information
- Strengthen AI access controls
- Improve model governance
- Find security weaknesses earlier
- Improve audit readiness
- Establish clearer AI accountability
- Support responsible AI adoption
- Improve AI vendor oversight
- Increase the effectiveness of lifecycle controls
- Increase security monitoring
- Reduce the risk of operational instabilities
- Help support AI adoption in a scalable way for enterprises AI adoption
Performance depends on the organisation's structure, quality of implementation operational model, risk management, and the ongoing maintenance.
Why Outsource AI Security, Governance & Compliance to ZechionAI?
AI Security + AI Engineering Understanding
AI security is more complex than the conventional cybersecurity skills.
It is a matter of understanding:
- Models
- Data
- APIs
- Infrastructure
- ML pipelines
- LLMs
- RAG
- AI agents
ZechionAI takes on AI security by examining both security and engineering perspectives.
- 01
Business-First Risk Management
We start with:
- Business goals
- AI use cases
- Risk
- Data
- Users
- Architecture
We then determine what controls are appropriate.
- 02
Technology-Neutral Recommendations
Our method can be based on:
- Existing enterprise platforms
- Cloud services
- Open-source technologies
- Commercial security platforms
- Custom controls
- Hybrid architectures
The goal is to match security strategies to the environment. security approach to surrounding environment and not create a hostile environment to conform to a specific product.
- 03
Security Across the AI Lifecycle
Security should be based on:
- Design
- Development
- Training
- Testing
- Deployment
- Monitoring
- Retirement
- 04
Generative AI & LLM Expertise
We are able to support:
- LLM security
- RAG security
- Prompt security
- AI agent security
- GenAI Governance
- LLM monitoring
- 05
Governance + Engineering
Governance is more effective when it is translated into:
- Policies
- Controls
- Workflows
- Documentation
- Monitoring
- Approval procedures
- 06
Independent Perspective
An outside team can provide an objective review of:
- Architecture
- Security
- Governance
- Vendor risk
- AI lifecycle control
- 07
Flexible Engagement
Companies can get involved with ZechionAI by:
- Assessments
- Consulting
- Project engagements
- Retainers
- Managed services
- Fractional security teams
- Specialized AI security teams
- 08
Continuous Improvement
AI environments change as companies implement:
- New models
- New vendors
- New AI applications
- New information
- New users
- New agents
- New infrastructure
AI security and governance needs to be able to evolve along with them.
When Not to Outsource
External support isn't always required.
Organizations that are mature
- Cybersecurity teams
- AI security specialists
- AI Governance programs
- Privacy teams
- Model-risk management
- Functions of compliance
- AI monitoring
- Engineering for security
might already have the skills they require.
In those instances, ZechionAI can provide specialized or uninvolved support, such as:
- Architecture reviews
- Independent evaluations
- AI red teaming
- LLM security testing
- AI security engineering
- Governance reviews
- AI risk assessments
- Vendor assessments
- Temporary capacity for specialists
- Support for specialized management
The goal is to enhance internal capabilities and not substitute for existing expertise.
Our AI Security & Governance Implementation Process
- 01
Discovery & AI Inventory
We have identified:
- AI systems
- Models
- Data
- Vendors
- APIs
- Users
- Infrastructure
- Business processes
- 02
Risk & Maturity Assessment
We evaluate:
- Security
- Privacy
- Governance
- Model risk
- Vendor risk
- Controls for operations
- 03
Threat Modeling & Gap Assessment
We are able to identify:
- Threats
- Vulnerabilities
- Control gaps
- Data risks
- Identity risk
- AI-specific attack scenarios
- 04
Architecture & Control Design
We define:
- Security architecture
- Governance framework
- Controls for access
- Monitoring
- Policies
- Risk controls
- 05
Implementation
We will implement the appropriate
- Security controls
- Governance workflows
- Monitoring
- Documentation
- Controls of identity
- Testing procedures
- 06
Validation & Testing
We confirm:
- Security controls
- AI applications
- Models
- APIs
- Data
- Governance workflows
- 07
Monitoring & Audit Readiness
We track:
- AI risks
- Security-related Events
- Model behavior
- Vendor risks
- Governance controls
- Evidence of compliance
- 08
Continuous Improvement
- 09
We regularly reassess
- AI use cases
- New models
- New threats
- New vendors
- New regulations
- Security posture
- Maturity of the governance
Engagement Models
ZechionAI doesn't publish any an arbitrary fixed price for every AI security engagement, as the scope of each engagement can be very different.
Prices can be influenced by:
- The number of AI systems
- Models available
- Complexity of the application
- Sensitivity to data
- Infrastructure
- Cloud environment
- LLM and its use
- AI agent capabilities
- Security needs
- The requirements for governance
- Scope of Compliance
- Vendor ecosystem
- Testing the scope
- The monitoring requirements
- Management-service requirements
Available Engagements
- AI Security Assessment
- AI Governance Assessment
- AI Risk Assessment
- AI Security Strategy
- AI Security Architecture
- AI Red Teaming
- LLM Security Assessment
- AI Agent Security Assessment
- AI Compliance Readiness
- AI Governance Implementation
- AI Security Engineering
- AI Audit Readiness
- AI Vendor Risk Assessment
- Managed AI Security
- Managed AI Governance
- Fractional AI Security
- Dedicated AI Security Team
AI Security & Governance for the U.S. Market
Organisations that operate within the United States may face different requirements regarding AI, based on their sector, use of the case, federal and state disclosure legal obligations regarding privacy, contractual obligations customers, and other situations.
ZechionAI thus employs a context-aware, risk-based approach instead of assuming a specific regulatory checklist is applicable to all company.
The NIST AI RMF is a useful non-committal risk management resource for businesses across industries, and its implementation is meant to be flexible and adapted to the specific needs of an organization and its demands. NIST is also aware that it's constantly upgraded, which underscores the importance of addressing AI governance as a constantly evolving discipline.
The requirements applicable to you may differ depending on the industry, the use of the case, location information, organizational conditions.
AI Security Is an Ongoing Discipline
AI security isn't an all-in-one security review.
AI models, systems and data, as well as applications, vendors and threats evolve in time, which makes continuous monitoring and management essential for numerous organizations.
The best approach could include:
- Existing security controls for cybersecurity
- AI-specific controls
- Cloud security
- Management of identities
- Protection of data
- Model governance
- Monitoring
- Testing
- Human supervision
- Organizational policies
- Vendor management
- Operational processes
Instead of relying on a single security platform or framework, you should consider relying on only one control.
They integrate technological security, AI risk management Governance processes, and responsible AI methods, security protections and monitoring, testing and compliance-readiness tasks around an company's AI systems.
AI introduces risky models as well as training data prompts, outputs, system for retrieval, AI agent, models providers and AI-specific attack strategies which may not be adequately handled by conventional security measures for application security all by itself.
AI governance sets guidelines, responsibilities, risk classifications, approval process documentation, monitoring human supervision, and lifecycle controls in AI systems.
AI security is primarily concerned with protecting AI technology models, data applications, identities and infrastructure. AI governance determines how an company handles AI risks and accountability, as well as oversight documentation, and decision-making.
Ideally, before AI becomes a part of the business process. However, companies are able to establish governance in any stage by first assessing the existing systems and deciding on more risky use cases.
Yes. A review can focus on models, applications as well as APIs, data flow identities, infrastructure pipelines, suppliers monitoring, governance and the appropriate controls.
Yes. ML security actions can deal with models artifacts, training pipelines dependences and data poisoning risk, models extraction threat, adversarial threats to access control, and monitoring.
Yes. Assessments can include prompts for context, data flow APIs providers, outputs access controls, the architecture of an application monitoring, and other appropriate security scenarios.
LLM security includes securing applications powered by LLM and their infrastructure from threats like quick injection, jailbreaks, vulnerable-data leaks, unauthorised use, unsafe access to tools and integration or provider threats.
Prompt injection is a security method in which the instructions embedded into user input or content retrieved attempt to alter the behavior of an AI system in unexpected ways, possibly overriding the intended behavior or control.
Yes. AI red teaming is able to simulate realistic scenarios of attack in the context of AI software, LLMs, RAG systems agents, APIs and other infrastructures in accordance with an agreed-upon area of.
Yes. Testing could include AI applications security test, API testing, model security testing and prompt-attack scenarios testing, penetration testing, vulnerability assessment and security regression testing when needed.
Yes. Agent assessments can be used to examine the identity of the user, permissions granted to tools authorisation, boundaries for actions as well as isolation, human approbation monitoring, logging and auditability.
Yes. RAG security can be used to address documents' permissions for retrieval, authorization to retrieve embeddings, vector databases knowledge-base governance sources integrity, poisoning of retrieval, and leakage of data.
Controls can include encryption, access control masking, tokenization, identity management, DLP lineage governance, data governance monitoring, and other appropriate policies for handling data.
Yes. We are able to evaluate AI data flow, sensitive information retention, access practices for processing, and privacy-related controls. Legal interpretation is the job of certified legal professionals.
Yes. Vendor assessments can take into consideration the handling of data, security, privacy, model behavior availability, subprocessors' retention, contracts, incidents response, dependencies, as well as exit issues.
Yes. Governance can be a part of AI policies role, risk classifications, roles inventory, approval workflows, documentation requirements managing change, monitoring and lifecycle process.
Yes. ZechionAI helps identify relevant controls, identify weaknesses in control, map controls, arrange evidence, and write documents. This isn't legal advice nor do we provide any assurance of compliance.
Yes. We can assist you in organizing AI inventories as well as risks assessments, policies controls, evidence documents, test results as well as governance records. audit-ready materials.
Yes. Based on the specific environment managed services could include security monitoring models, model governance, risk monitoring monitors vendor performance, reporting, maintenance of policies, and periodic reviews.
The length of time depends on the extent. A specific assessment of one AI application could be significantly smaller than an enterprise-wide evaluation that covers multiple vendors, models environments, models, and business divisions.
Pricing depends on the quantity and the complexity of AI models, systems, data sensitivity, the infrastructure testing scope and vendor requirements for governance and whether continuous managed services are needed.
Yes. AI security is able to supplement existing security cloud security, IAM, application security as well as data security, privacy SOC, GRC, and incident-response functions instead of acting as a separate software.
Yes. Based on your needs, ZechionAI can provide managed AI security as well as governance, risk monitoring as well as vendor risk management, reporting, policy maintenance and specific AI security engineering.
Secure AI. Govern AI. Deploy AI Responsibly
Your business doesn't need to decide between AI innovations and a responsible risk management.
Talk to Zech, live