Govern AI usage, control agent access, and monitor performance at scale. Kizzy’s AI Ops & Governance
services bring policy, security, and visibility to enterprise AI and Agentforce adoption.
AI Operations (AIOps) is the enterprise framework for deploying, managing, and governing artificial intelligence systems and AI agents in production. It helps enterprise IT and data leaders ensure continuous performance, compliance, and cost control.
Kizzy Consulting is a premier Salesforce Ridge Partner offering AIOps, AI Governance, Agentforce implementation, AI Risk Management, and LLMOps strategies.
AIOps & Governance services help organizations confidently scale AI through infrastructure-ready solutions that combine intelligent automation with built-in compliance, EU AI Act readiness, and strict access controls. We transform fragmented AI experiments into a governed, enterprise-grade digital workforce.
Artificial Intelligence for IT Operations. Using AI to automate, streamline, and optimize IT service management and operational workflows.
Machine Learning Operations. Focuses on the backend pipeline of training, validating, and deploying traditional predictive ML models and managing data drift.
The subset of AIOps governing generative models (LLMs) and autonomous agents, focusing on prompt versioning, semantic tracking, and API tool-calling boundaries.
As enterprises rapidly adopt generative AI, usage often outpaces governance. This leads to "Shadow AI," escalating token costs, and dangerous security vulnerabilities.
AIOps leverages big data, analytics, and ML capabilities to separate significant event alerts from the "noise." By integrating disparate AI models and Agentforce configurations into a single, intelligent IT operations platform, AIOps enables IT operations teams to respond quickly - and proactively - to model degradation or anomalous agent behavior.
While DevOps focuses on accelerating software deployment, AIOps acts as the control tower. When used in tandem, AIOps and DevOps services help businesses create a comprehensive approach to managing the entire software and AI lifecycle without sacrificing regulatory integrity.
Governance is embedded by design - ensuring every model rollout is traceable, bias-monitored, and policy-aligned. Security isn’t an afterthought; it’s integral to every layer.
We architect secure, scalable AI infrastructure tailored to enterprise environments - on-prem, cloud, or hybrid. From CI/CD pipelines to environment provisioning, we ensure model deployment is automated, consistent, and auditable across the board. This foundation reduces time-to-production and ensures environment parity for compliance.
We build AI infrastructure with zero-trust at its core. Encrypted model repositories, tamper-proof audit logging, and strict data exfiltration barriers protect your IP. Whether safeguarding training data or restricting API execution rights for autonomous agents, our approach meets stringent industry standards.
Continuous monitoring of LLM token consumption to enforce budget guardrails. We implement dynamic routing (sending simple tasks to cheaper models) and semantic caching to eliminate redundant API calls, keeping cloud computing costs highly predictable.
AI models don’t stand still - we ensure you’re ready when they move. Our observability framework continuously monitors live models for data drift, behavioral drift, and compliance issues. With feedback loops, automated performance alerts, and auto-remediation options (kill-switches), enterprises adapt in real-time.
Because our managed AI governance and AIOps service provides the control tower for your autonomous operations.
We ensure every decision an agent makes is auditable, compliant, and optimized for performance. We guarantee that your custom LLMs and bespoke Agentforce agents operate securely and cost-effectively, day and night.
24/7 dashboards for real-time visibility into agent activity, decision pathways, API latency, and system health across your tech stack.
Automatic logging of every prompt, response, and agent action to meet internal policies and external regulatory standards (e.g., ISO 42001).
Maintaining safety constraints to prevent unauthorized API actions, stopping data exfiltration before it happens with API Action Whitelisting.
A successful AI strategy requires a robust operational backbone. Our governance framework is designed to manage the unique risks and opportunities of a scaled AI workforce.
Start Your ImplementationWe define the rules of engagement. We work with you to set budgets, define agent permissions, and establish non-negotiable compliance and safety guardrails.
We implement the "virtual control tower." Our AIOps dashboards are configured to provide 24/7, real-time visibility into agent performance, latency, and cost.
We actively enforce the guardrails. Our infrastructure continuously monitors for anomalies, audits agent decision logs for compliance, and manages API tool-calling boundaries.
We ensure your agents get smarter. We manage model versioning, conduct performance tuning, and adapt semantic caching to improve agent efficiency continuously.
Governance refers to the processes, standards, and guardrails that ensure AI systems are safe, ethical, and bias-free. Without it, enterprises risk financial, legal, and reputational damage.
AI models are trained on human data and are susceptible to human biases. Left unchecked, systems influenced by erroneous bias can lead to discriminatory conclusions. We've seen high-profile failures - like chatbots parroting toxic behavior or inherent biases in algorithms affecting financial and legal decisions.
Effective oversight mechanisms foster innovation while building trust. Implementing a modern AI governance policy helps AI adhere to moral values and reduces risk levels across broad applications.
Governance maturity varies by enterprise. We transition organizations to Level 3:
AI governance is no longer optional. Global legislative frameworks dictate strict transparency and data handling requirements. Kizzy ensures your AIOps infrastructure is audit-ready.
| Regulation / Framework | Region | Primary Mandate & Impact | Status |
|---|---|---|---|
| The EU AI Act | European Union | Takes a risk-based approach. High-risk systems require strict governance and transparency. Penalties reach up to 35M EUR or 7% of global turnover. Governs General Purpose AI (GPAI). | Enforced (2026) |
| Fed SR-26-2 (Replaces SR-11-7) | United States (Finance) | Federal Reserve's revised guidance on Model Risk Management. Demands explicitly risk-based methodologies for AI in banking, proving models have not drifted. | Active |
| Directive on Automated Decision-Making | Canada | Uses a scoring system to assess human intervention and peer review needed. High-score solutions require failsafes, public notice, and algorithmic impact assessments. | Adopted |
| GDPR & OECD Guidelines | Global | Limits automated decision-making regarding personal data. Promotes responsible stewardship of trustworthy AI, fairness, and transparency across 40+ countries. | Active |
| APAC Frameworks | Asia-Pacific | Singapore's 2026 AI governance model framework for agentic AI. Developing guidelines in China, India, and Japan regarding generative IP and portrait rights. | Evolving |
Founder & CEO, Kizzy Consulting · 13× Salesforce Certified Architect
Sanjeet leads Kizzy Consulting's AIOps & Governance practice globally, establishing enterprise AI Operations frameworks, LLM gateway implementations, and observability layers grounded in strict data compliance protocols (ISO 42001, EU AI Act).
Connect on LinkedInDeep-dive answers on enterprise AI governance, Agent Ops, monitoring, and compliance.
AI Operations (AIOps) is the strategic and technical framework used by enterprises to manage the entire lifecycle of artificial intelligence applications and autonomous agents.
It bridges the gap between AI development and production, ensuring that deployed models remain secure, cost-effective, and fully compliant with governance frameworks. Beyond basic hosting, AIOps includes continuous monitoring, incident management, automated testing, and fine-tuning. This structured approach allows enterprises to scale AI innovations safely, transitioning from experimental sandboxes to mission-critical, enterprise-grade deployments.
While MLOps focuses on the backend processes of training, validating, and deploying traditional machine learning models (predictive AI) and managing data drift, AIOps is a broader discipline designed primarily for managing operations, generative AI, and autonomous agents in production.
AIOps deals with entirely new operational challenges such as prompt engineering version control, token cost optimization, LLM routing, and managing non-deterministic outputs. It integrates LLMOps strategies to govern conversational interfaces and complex tool-calling actions.
AI governance is the foundational policy layer of AI Operations, translating business rules into executable code. It establishes strict parameters - such as acceptable use cases, risk tiering, and data access controls - before an AI agent goes live.
In practice, this involves implementing an LLM Gateway that enforces role-based access controls (RBAC), restricts agents from accessing sensitive Personally Identifiable Information (PII), and mandates 'human-in-the-loop' approvals for high-stakes actions, ensuring compliance with regulations like the EU AI Act.
AI Agents (like Salesforce Agentforce) are the active workforce of modern deployments, executing multi-step tasks and calling APIs. Within AIOps, these agents require rigorous Agent Governance and Agent Monitoring.
AIOps provides the infrastructure to define precise behavioral boundaries, manage access credentials to enterprise systems (ERPs, CRMs), and orchestrate their lifecycles - preventing agents from acting independently outside approved scopes.
AI Incident Management is the systematic process of identifying, logging, diagnosing, and resolving issues when an AI model misbehaves in production (e.g., generating biased output, leaking data, or infinite API loops).
Effective protocols include automated alerts for anomalous behavior, immediate kill-switches to revert to human agents, and detailed post-mortem analyses of prompt logs to deploy updated guardrails.
Stop flying blind with AI deployments. We establish the governance frameworks, LLM gateways, and observability dashboards you need to scale AI safely and stay compliant with 2026 regulations.
Fill this in and our Enterprise AI lead will follow up within one business day.
Learn about the key requirements, duties, responsibilities, and skills that should be in a business development executive job description.
Business development executives are responsible for driving company sales by sourcing new clients, and by convincing existing clients to purchase added offerings. As such, business development executives play an integral role in companies’ longevity.