Key Takeaways
- In this article, we cover products that protect live AI applications at runtime, not consulting, testing-only, or employee-access tools. Runtime protection inspects prompts, responses, and, increasingly, agent tool calls, and blocks attacks as they happen.
- India's own threat data supports the urgency. The Digital Threat Report 2025-26, released by MeitY with CERT-In, CSIRT-Fin and SISA, found that six of seven predictions from the previous edition had already reached full-scale realization, and describes LLM prompt injection as now operational, not theoretical.
- Architectures differ. Some products enforce inline at the network layer with no application changes, some integrate through an API or SDK, and some are modules of a larger security platform. These choices affect latency, deployment effort, and what the product can see.
- Runtime protection and red teaming are complementary. RBI's FREE-AI framework recommends red teaming at least twice a year for medium- and high-risk AI applications.
- Verify before buying: inline enforcement, model and framework compatibility, agent and MCP coverage, deployment options, data handling, and commercial availability in India. This comparison is based on public documentation, not hands-on testing.
The best AI security provider for an Indian enterprise depends on how the application is built and where it runs. Prophaze suits teams that want a model-agnostic reverse proxy in front of LLM applications. Palo Alto Networks, Zscaler, Cisco, SentinelOne, and Check Point offer runtime protection as part of broader security platforms, with different enforcement models. Prophaze delivers runtime protection through its AI security platform, and this guide explains how to choose.
Why Runtime Protection Matters for Indian Organizations
The Digital Threat Report 2025-26 is the most authoritative India-specific source available. Its findings that bear on AI buyers include:
- It names prompt injection as an operational runtime threat, and notes that agentic AI often operates with admin-level privilege under service-account-grade governance.
- Its defensive priorities include adversarial robustness testing before AI deployment, treating AI agents as privileged identities under continuous monitoring, and prompt-injection-aware content ingestion controls.
- For AI and ML workloads, it notes that attestation typically shows encryption at rest, while the real exposures are model poisoning, adversarial probing and inference-time exposure. The evidence it asks for includes inference monitoring and prompt abuse detection, which is what runtime products provide.
Prompt injection is also the top-ranked risk in the OWASP Top 10 for LLM Applications. For market context, see the analysis we have of recent AI security market trends.
For Indian organizations, AI runtime security is essential to prevent prompt injection, data leakage, and unauthorized agent actions, helping protect sensitive information, reduce operational risks, and support secure AI adoption.
Top 6 AI Security Providers In India
1. Prophaze AI LLM Security
Our AI LLM Security is deployed as a reverse proxy in front of chat endpoints, RAG services, agent orchestrators, and API gateways. It inspects every prompt in the request body and query arguments before it reaches the application or model. Because enforcement is at the network layer, the same protection applies to OpenAI, Anthropic, Bedrock, Azure OpenAI or a self-hosted open-weight model, with no SDK, no application code change and no modification to system prompts.
And it currently blocks prompt injection (OWASP LLM01:2025) across 53 subcategories and more than 4,400 attack variants, normalizes encoded and obfuscated input before evaluation, and applies its web and API rule set to the same traffic.
2. Palo Alto Networks Prisma AIRS
Palo Alto Networks Prisma AIRS provides AI runtime security through Network Intercept, which enforces inline at the network layer without application-code changes, and API Intercept, which integrates through a REST API or Python SDK. Its capabilities include prompt injection and jailbreak prevention, agent protection against memory poisoning and tool misuse, sensitive data leakage prevention, malicious URL and malware detection, content moderation, and MCP traffic inspection.
Consideration: Buyers should verify feature availability across deployment modes, traffic-routing requirements, integration complexity, licensing, and runtime performance.
3. Zscaler AI Guard
Zscaler AI Guard applies inline inspection to AI interactions, offering protections against prompt injection and jailbreaks, detection and blocking of personal and confidential data in prompts and responses, content filtering, and shadow AI discovery. Zscaler reports more than 18 specialized detectors and multilingual support, with enforcement options including allow, block, and coach.
Consideration: Buyers should validate detector coverage, enforcement granularity, multilingual accuracy, supported AI applications, MCP protection, and compatibility with their existing Zscaler deployment.
4. Cisco AI Defense
Cisco AI Defense combines AI asset visibility, algorithmic red teaming across more than 200 threat subcategories, and runtime protection for prompts and responses against threats such as prompt injection and sensitive data leakage. It also supports MCP security, inspecting agent actions and tool calls, with SDK-based integration options for application-level inspection.
Consideration: Buyers should verify SDK integration requirements, supported frameworks, runtime latency, MCP coverage, and how effectively red-team findings translate into enforceable production controls.
5. SentinelOne Prompt Security
SentinelOne Prompt Security provides AI application security spanning pre-production testing and runtime protection, including bidirectional prompt and response inspection, red teaming, sensitive-data protection, employee AI usage controls, and shadow AI discovery. Its positioning emphasizes integrating AI security into the broader SentinelOne security ecosystem.
Consideration: Buyers should validate the claimed single-line deployment, red-team-to-runtime integration, supported AI frameworks, false-positive rates, and the extent of integration with existing SentinelOne products.
6. Check Point AI Guardrails
Check Point AI Guardrails provides API-oriented protections against direct and indirect prompt injection, jailbreaks, sensitive-data leakage, and system-prompt exposure. It supports configurable detection thresholds and daily updates to managed guardrails, while Check Point’s broader AI Agent Security offering extends protection to agent workflows and tool interactions.
Consideration: Buyers should assess integration effort, policy customization, false-positive rates, MCP and agent-security coverage, and workload-specific latency, since performance varies with content size and the number of detectors enabled.
What to Verify Before You Consider The Best AI Security Solution For Your Stack
- Enforcement point: inline proxy or firewall, API call or SDK wrapper. Each changes latency, deployment effort and failure behavior.
- Model and framework compatibility: does it work with your hosted and third-party models?
- Coverage: prompt injection (direct and indirect), jailbreaks, sensitive-data leakage, malicious URLs, and agent tool calls and MCP traffic.
- Deployment and data handling: where prompts, responses and logs are processed and stored. CERT-In directions require ICT logs to be kept within Indian jurisdiction for 180 days, so ask about India hosting and log export.
- Evidence: logs that show category, severity and action for each decision, to support audits. The Digital Threat Report lists adversarial testing results, inference monitoring and prompt abuse detection among the evidence needed.
- Commercial availability: Check how their licensing, support hours and pricing works in India.
- Proof of concept: replay your own attack set, measure false positives and added latency, and test encoded and obfuscated payloads. For a framework, see our latest article on AI security strategy guide.
AI Red Teaming vs. Runtime Protection for Indian Businesses
RBI’s FREE-AI report recommends structured red teaming across the AI lifecycle, at least twice a year for medium- and high-risk applications and before major model updates, with findings documented for audit. Several of the platforms above include red teaming components, including Cisco, SentinelOne and Check Point’s Lakera Red. Prophaze does not provide red teaming services. Red teaming finds weaknesses; runtime protection blocks exploitation while fixes are built, so most regulated organizations will want both.
Runtime Security for GenAI Chatbots in India
For customer-facing GenAI chatbots, prioritize low-friction runtime protection that blocks prompt injection and system prompt extraction before the model is called, plus checks on responses for personal data. Inline proxies suit teams that cannot change application code; API or SDK integrations suit teams that want policy decisions inside the application flow. Whichever you choose, test indirect injection through retrieved documents and uploads, not just typed prompts.
AI Security Pricing in India: Costs, Licensing and Budget Considerations
Most of these vendors quote on request, and public pricing is limited. One public data point from a third-party comparison is that Lakera Guard has a free community tier of 10,000 requests per month, which is useful for a proof of concept but not enterprise pricing; confirm current terms with Check Point. For like-for-like quotes, ask each provider to price the same scope: applications or endpoints covered, request volume, agents and MCP servers, red teaming rounds, log retention, and support hours.
For affordable AI security in India, protect your highest-risk application first, such as one customer-facing chatbot, then expand. Platform bundles can be cost-effective if you already use the vendor’s firewall, SASE or endpoint products, and expensive if you do not.
What Indian Businesses Need to Know About Emerging AI Security Risks
AI security is rapidly evolving as vendors consolidate, AI agents gain access to enterprise systems, and regulators place greater emphasis on risk management.
Vendor consolidation
Check Point, SentinelOne, Cato Networks and F5 have expanded their AI security portfolios through acquisitions of Lakera, Prompt Security, Aim Security and CalypsoAI, respectively. Buyers should assess how these acquisitions affect product roadmaps, licensing, support and standalone availability.
AI agent security
Check Point announced an integration with Google Cloud’s Gemini Enterprise Agent Platform, including runtime guardrails and tool-call screening through Agent Gateway. This reflects a broader shift toward securing AI agents within the platforms where they operate.
India's cybersecurity priorities
India’s Digital Threat Report 2025ā26, released in July 2026, highlights emerging AI-related risks to the banking, financial services, insurance and payments ecosystem. The RBI’s FREE-AI framework also emphasizes structured AI risk assessment and red teaming.
Regional regulatory developments
Singapore’s Monetary Authority issued its AI Risk Management Guidelines on 7 October 2026, with implementation beginning in October 2027. These developments are relevant to Indian financial institutions operating in Singapore.
For buyers, the key takeaway is to evaluate not just current capabilities, but also vendor roadmaps, integration options and long-term product support.
Strengthening AI Security with API and WAAP Protection
AI runtime security is only one layer of protection. GenAI applications also depend on web interfaces, APIs, data sources and backend services, each of which can introduce vulnerabilities. Combining AI-specific guardrails with Web Application and API Protection (WAAP) helps address both AI-driven threats and conventional application attacks.
Organizations can explore our latest API and WAAP datasheets, review our recently launched AI Threat report use WAAP Security ROI Calculator to assess potential business value.
Indian organizations should combine AI runtime controls, application and API security, and continuous testing to reduce risk as AI adoption grows. Start with the applications and agents handling the most sensitive data or critical workflows, then expand protection based on measurable risk.
- Ready to Shortlist an AI Runtime Security Provider?
See how Prophaze blocks prompt injection in front of your LLM applications and what to pair it with for response and agent controls.
Frequently Asked Questions (FAQ)
1. What is the difference between AI runtime security and AI security consulting?
AI runtime security is a product that sits in the request path and inspects prompts and responses to block attacks as they happen. Consulting and assessment services test or advise on your AI systems on a schedule. Most organizations need both, but only runtime products stop an attack in progress.
2. Which AI security provider is best for Indian enterprises?
There is no single best provider. Prophaze fits teams wanting a model-agnostic reverse proxy with no code changes, while Palo Alto Networks, Zscaler, Cisco, SentinelOne and Check Point fit enterprises that already use those platforms or want broader agent and red teaming coverage. Verify India hosting, support, and pricing with each.
3. Should AI runtime protection be an inline proxy or an API integration?
Inline proxies and network-layer enforcement need no application code changes and see all traffic, while API or SDK integrations place checks inside the application flow and can be more granular. The right choice depends on your architecture and team. Test added latency and failure behavior for either approach in a proof of concept.
4. Do Indian banks need both runtime protection and red teaming?
RBI’s FREE-AI report recommends structured red teaming across the AI lifecycle, at least twice a year for medium- and high-risk AI applications. Runtime protection blocks attacks in production, while red teaming finds weaknesses in advance. Confirm current supervisory expectations with your compliance team.
5. How much does AI runtime security cost?
Pricing is usually quoted on request and depends on applications covered, request volume, agents and MCP servers, and support hours. A free community tier exists for at least one product, which helps with trials. Ask each vendor to price an identical scope in INR, so quotes are comparable.
