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AI SECURITY & GOVERNANCE

Secure AI From 

Discovery to Deployment

Gain visibility into AI assets, identify risk, test models and applications, enforce runtime protection, and establish governance across your AI environment.


Explore AI Security Solutions

THE AI SECURITY CHALLENGE

AI innovation creates a new security landscape.

Models, agents, datasets, applications, and AI development environments are spreading rapidly across the enterprise. Security teams need visibility and control without becoming a barrier to innovation.

01 - VISIBILITY

Limited AI Visibility

AI projects, models, agents, datasets, and tools can operate outside traditional security controls.

02 - EXPOSURE

New Attack Surfaces

Prompts, models, APIs, autonomous agents, MCP servers, and AI supply chains create new attack paths.

03 - GOVERNANCE

Growing Governance Requirements

Organizations need policy, accountability, controls, and evidence to demonstrate responsible AI use.

COMPLETE AI SECURITY & GOVERNANCE

Protect the entire AI lifecycle.

From the first AI asset introduced into your environment through production deployment and ongoing governance.

01 - DISCOVERY

AI Discovery & Inventory

Know where AI is being used.

Continuously discover and inventory AI projects, models, datasets, notebooks, applications, agents,MCP servers, endpoints, and related resources.

  • Discover AI assets across platforms
  • Identify unmanaged and unapproved AI
  • Track ownership, lineage, and risk
  • Build a centralized AI inventory

02 - POSTURE

AI Security Posture Management

Find risk before it becomes exposure.

Assess AI environments and deployments for security weaknesses, misconfigurations, inappropriate permissions, and data exposure.

  • Detect security misconfigurations
  • Identify risky AI configurations
  • Monitor AI infrastructure
  • Protect sensitive and regulated data

03 - TEST

AI Red Teaming & Model Testing

Test AI before attackers do.

Continuously evaluate models, agents, and connected applications for vulnerabilities, unsafe behavior, and adversarial attacks.

  • Automated AI red teaming
  • Model vulnerability testing
  • Prompt injection testing
  • Connected application testing

04 - PROTECT

AI Runtime Guardrails

Protect AI while it operates.

Monitor and control AI interactions to help prevent malicious input, sensitive-data exposure, unsafe content, and policy violations.

  • Monitor prompts and responses
  • Detect prompt injection and jailbreaking
  • Identify unsafe content
  • Block, mask, or redact based on policy

05 - AGENTS

AI Agent & MCP Security

Secure autonomous AI and agentic workflows.

Discover agents, understand what they can access, protect MCP connections, and control tools and permissions.

  • Discover agents and MCP infrastructure
  • Map agent connections and activity
  • Control tools and permissions
  • Apply policy-based guardrails

06 - APPLICATIONS

AI Application Security

Secure the applications that use AI.

Bring application and AI security together across the software development lifecycle.

  • Consolidate security findings
  • Correlate AI and application risk
  • Prioritize vulnerabilities
  • Enforce policy-based CI/CD gates

07 - GOVERN

AI Governance & Compliance

Turn AI policy into operational controls.

Establish governance across AI projects while maintaining the visibility and evidence needed to support compliance.

  • Define and enforce AI policies
  • Manage approvals and exceptions
  • Maintain oversight and accountability
  • Track AI risk
  • Support regulatory and framework alignment

CONTINUOUS AI PROTECTION

One security lifecycle from discovery through governance.

Protect AI across its lifecycle with a connected approach to visibility, risk assessment, testing, runtime protection, and governance.

01

Discover

Models, agents, data, applications, notebooks, and infrastructure.            

02

Assess

Posture, configuration, exposure, supply chain, and data risk.                    

03

Test

Models, prompts, agents, integrations, and AI-enabled applications.  

04

Protect

Guardrails, APIs, data, MCP, runtime activity, and policy enforcement.   

05

Govern

Policies, approvals, accountability, compliance evidence, and reporting.

GOVERNANCE & COMPLIANCE

Build trust into AI adoption.

Security is only part of responsible AI adoption. Organizations also need governance, accountability, policy enforcement, and evidence that AI systems operate within defined requirements.

NIST AI RMF ISO 42001OWASP

GOVERNANCE

Establish accountability

Define ownership, policies, approvals, and decision rights.

RISK

Understand AI exposure

Prioritize technical, operational, and data-related risk.

COMPLIANCE

Support alignment

Map controls and evidence to standards and requirements.

OVERSIGHT

Monitor continuously

Track changes in posture, usage, and AI resources over time.

SECURE AI WITH CONFIDENCE

AI Security Technology. Regional Expertise.

iAlmas combines advanced AI security and governance technology with regional expertise, implementation support, and ongoing customer engagement.







Whether you are establishing an AI governance program or already deploying models, applications, and agents into production, we can help you identify risk, implement the right controls, and build a more secure path to AI adoption.

Talk to an AI Security Specialist


Technology

Enterprise AI security and governance powered by PointGuard AI.

Expertise

Support for requirements, architecture, deployment, and adoption planning.

Partnership

Regional engagement and ongoing support across the customer lifecycle.