AI has unlocked enormous opportunities, but it has also introduced new risks that traditional security tools cannot handle. From prompt injection attacks and shadow AI to unauthorized data retrieval, enterprises are now facing AI-native threats. That is where the AI Security Platform (AISP) comes in.
AISPs are quickly becoming the cornerstone of modern cybersecurity. Gartner projects that by 2028, more than 50% of enterprises will use an AI Security Platform to secure both employee use of third-party AI services and custom-built AI applications.
If you are evaluating how to protect your organization’s AI investments, this guide explains:
- What an AI Security Platform (AISP) is
- Why it matters now
- Core capabilities every AISP should deliver
- Benefits of adopting an AISP
- How to evaluate solutions in this fast-evolving market
- How BigID helps enterprises cover the full spectrum of AI security
What Is an AI Security Platform (AISP)?
An AI Security Platform (AISP) is a unified solution designed to protect against AI-native security risks such as prompt injection or model abuse, and AI-amplified risks such as data leakage from misconfigured cloud storage.
Unlike point solutions, AISPs provide a platform approach that consolidates multiple security functions into one trusted system. This is critical as most enterprises are already managing more than 40 separate security tools.
Why AI Security Platforms Matter Now
AI adoption has exploded. Generative AI and agentic AI are being embedded in everything from productivity suites to customer-facing applications. With that growth comes a new attack surface.
Key challenges driving the need for AISPs:
- Shadow AI: Employees using unapproved AI services without governance
- Prompt Injection and Jailbreaks: Malicious prompts that trick AI into leaking data or executing harmful actions
- Toxic Outputs and Hallucinations: Unreliable AI responses that can damage trust, compliance, or brand reputation
- Unauthorized Data Access: Retrieval-augmented generation (RAG) pulling sensitive data without proper controls
- Model Supply Chain Risks: Downloaded or fine-tuned models carrying hidden vulnerabilities
Core Capabilities of an AI Security Platform
A modern AISP should deliver end-to-end coverage for both AI consumption and development.
AI Usage Control and Discovery
- Prevents data leakage to unapproved AI tools
- Blocks or redacts sensitive inputs and outputs
- Identifies and mitigates shadow AI
- BigID: Discover and classify all AI usage across SaaS and cloud to expose shadow AI and enforce acceptable-use controls
LLM Security Guardrails
- Detects and prevents prompt injection, jailbreaks, and system prompt leakage
- Filters toxic or noncompliant outputs
- BigID: Apply identity-aware guardrails that tie access and prompts back to real user entitlements, improving accuracy and compliance
RAG Security
- Ensures retrieval only pulls data the user is authorized to access
- Protects vector databases against injection and leakage
- BigID: Context-aware authorization combined with fine-grained data sensitivity and owner mapping ensures RAG only retrieves what each user is entitled to see
Automated AI Security Testing and Red Teaming
- Continuously stress-tests AI models against evolving attacks
- Provides real-world resilience metrics
- BigID: Build automated AI risk assessments and continuously measure exposure
Model Scanning and Inventory
- Detects malicious code in downloaded or open-source models
Builds visibility into AI and LLM usage across the organization - BigID: Extend AI supply chain security with integrated model inventory, validation, and lineage across open-source and enterprise AI
Benefits of Adopting an AI Security Platform
- Unified Protection: One platform to manage AI risks across employees, apps, and infrastructure
- Reduced Tool Sprawl: Consolidates fragmented point tools into a single system
Data Security and Privacy: Addresses the top priority for enterprises adopting GenAI - Compliance and Governance: Enforces AI acceptable-use policies and regulatory requirements
- Trust and Adoption: Builds confidence in AI initiatives by demonstrating safety and reliability
- BigID: Brings all these benefits together by combining AI discovery, security, and governance into a single enterprise platform
How to Evaluate an AISP
When assessing vendors, consider:
- Coverage: Does it span AI usage, guardrails, RAG, testing, and model scanning
- Integration: Can it augment existing CNAPP, DSPM, SSE, and hyperscaler-native guardrails
- Innovation Depth: How advanced are its detection methods (for example, beyond regex into AI-powered classification)
- Trustworthiness: Is the platform transparent, security-by-design, and aligned with NIST or AI TRiSM frameworks
- Deployment Flexibility: SaaS, hybrid, or on-prem depending on enterprise needs
- BigID: Delivers broad coverage and deep integration, uniquely combining DSPM, DSP, and AI TRiSM into a single extensible architecture
The Bottom Line
The AI Security Platform (AISP) is not optional, it is foundational. As AI adoption accelerates, fragmented tools will fail to keep up. The winners will be platforms that deliver trust, visibility, and control across the entire AI lifecycle.
BigID helps enterprises get there by addressing every dimension of AI security: usage, guardrails, retrieval, testing, and supply chain. The result is safer and faster AI adoption at scale with stronger trust, compliance, and business value.
