AI SECURITY
Adopt AI with Confidence. Protect What Matters.
Bring AI into your business with security built in. Primenet Limited helps organizations protect AI applications, safeguard sensitive data, and manage the risks of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI adoption.
From employee copilots and customer-facing chatbots to enterprise knowledge assistants and connected AI agents, our approach helps you strengthen protection across your AI environment.
Security for Every Stage of AI Adoption
AI creates opportunities to improve productivity, automate workflows, and serve customers more effectively. It also introduces new paths to sensitive information and business systems.
Our AI Security Solutions bring together discovery, protective controls, adversarial testing, and operational monitoring. We help you understand where AI is used, what it can access, and how to manage the risks that matter to your organization.
Four pillars, one approach
01 Discovery
Know where AI is used and what it can access.
02 Protective controls
Safeguard data, applications, and agents.
03 Adversarial testing
Validate defenses with authorized attack simulation.
04 Operational monitoring
Detect, investigate, and respond to AI threats.
Our AI Security Capabilities
Eight connected capabilities that help you discover, protect, test, and operate AI securely—from first use case to production.
01
Enterprise AI Discovery & Governance
Build visibility into AI tools, applications, models, and agents—including Shadow AI (AI tools employees use without organizational approval). Establish ownership, acceptable-use policies, and risk priorities to support informed adoption decisions.
- ✓ Identify AI use cases, owners, integrations, and data flows.
- ✓ Review employee use of approved and unapproved AI tools.
- ✓ Define access policies, accountability, and escalation procedures.
Business value: Clearer visibility and stronger control over enterprise AI adoption.
02
Generative AI & LLM Application Security
Strengthen protection for chatbots, copilots, and applications powered by large language models (LLMs). Address prompt injection (hidden or malicious instructions that manipulate an AI system), sensitive information exposure, and unsafe output handling through layered controls and targeted testing.
- ✓ Assess model inputs, outputs, and application trust boundaries.
- ✓ Introduce appropriate filtering, validation, and runtime safeguards.
- ✓ Evaluate protections against realistic business workflows.
Business value: Safer AI interactions for employees and customers.
03
AI Data & Knowledge Protection
Reduce the risk of confidential information being exposed through AI systems. Protect customer records, business documents, credentials, and intellectual property throughout supported AI workflows.
- ✓ Review data handling and retention requirements.
- ✓ Apply access controls to Retrieval-Augmented Generation (RAG) knowledge sources—the document stores an AI assistant searches to answer questions.
- ✓ Assess filtering and redaction requirements for prompts, outputs, and logs.
Business value: Better protection for enterprise knowledge and sensitive information.
04
Agentic AI & Identity Security
Control what AI agents can access and do. Review connected tools, APIs, identities, and Model Context Protocol (MCP) integrations—an open standard that connects AI agents to business tools and data—to limit unauthorized activity.
- ✓ Apply least-privilege access and appropriately scoped credentials.
- ✓ Define approval steps for sensitive actions.
- ✓ Record agent activity to support investigation and accountability.
Business value: Greater confidence in automation that interacts with business systems.
05
AI Red Teaming & Adversarial Validation
Evaluate AI applications through authorized attack simulations. Test how systems respond to malicious instructions, attempts to expose protected information, and misuse of connected tools.
- ✓ Assess prompt injection and attempts to bypass safeguards.
- ✓ Test access boundaries and agent permissions.
- ✓ Deliver prioritized findings, remediation guidance, and agreed retesting.
Business value: Evidence to guide improvements before and after deployment.
06
Secure MLOps & LLMOps
Build security into the development, deployment, and operation of AI and ML systems. Connect machine learning operations (MLOps), LLM operations (LLMOps)—the practices for reliably deploying, monitoring, and updating AI models—and security practices across the delivery lifecycle.
- ✓ Review model repositories, dependencies, data pipelines, and deployment permissions.
- ✓ Introduce model and prompt versioning, automated evaluations, and release approvals.
- ✓ Establish monitoring and rollback procedures for supported deployments.
Business value: More reliable releases and stronger accountability as AI systems evolve.
07
AI Detection & Response
Connect AI security activity with monitoring, investigation, and incident response. Develop coverage suited to your applications, available telemetry, and security operations.
- ✓ Monitor supported AI interactions and security events.
- ✓ Investigate suspicious behavior and policy violations.
- ✓ Develop response playbooks and integrate relevant events with Security Operations Center (SOC) workflows.
Business value: Better visibility into AI threats and a clearer path to response.
08
AI-Assisted Security Operations
Use AI and ML capabilities to support security analysts. Introduce controlled automation for repetitive tasks while retaining human oversight of consequential decisions.
- ✓ Support alert prioritization and investigation summaries.
- ✓ Enrich security events with relevant context.
- ✓ Develop approved automation workflows with audit trails and review points.
Business value: More analyst capacity for investigation, judgment, and improvement.
Our Approach
A clear, four-stage engagement model—from first assessment to day-to-day operation.
STEP 1
Assess
Identify AI assets, data flows, business requirements, and risks.
You receive: An agreed scope and prioritized risk assessment.
STEP 2
Protect
Implement controls across supported applications, data, and integrations.
You receive: Documented safeguards and configuration changes.
STEP 3
Validate
Test protections and review residual risks.
You receive: Evaluation results and prioritized improvements.
STEP 4
Operationalize
Connect monitoring, response, and ownership.
You receive: Playbooks, handover documentation, and an improvement roadmap.
Connected to Your Wider Security Strategy
AI security works best when it connects with the defenses your business already relies on. Primenet Limited brings together application security, cloud security, vulnerability assessment, network security, and security operations to support a coordinated approach to AI adoption.
Frequently Asked Questions
What does AI security cover?
AI security addresses risks involving AI applications, models, data, integrations, identities, and agent actions. The appropriate scope depends on how your organization uses AI.
How does AI security relate to traditional cybersecurity?
Traditional controls remain essential. AI security adds assessment and protection for issues such as instruction manipulation, inappropriate information disclosure, and misuse of AI-connected tools.
Can AI security integrate with our existing SOC?
Integration can be designed around supported platforms, available telemetry, and agreed response procedures. The assessment establishes what can be monitored and what additional controls are needed.
Does an assessment guarantee that an AI system is secure?
No. An assessment provides evidence about the systems and scenarios tested. Ongoing evaluation is needed as models, applications, data, and threats change.
Ready to Secure Your AI Environment?
Discuss your AI use cases, understand your exposure, and plan practical steps toward safer adoption.
or email sales@primenetbd.net