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Aquila I AIS DR
Your staff adopted AI before your security stack did. You can still see what it touches and stop it misbehaving — Aquila I AIS DR discovers the AI tools, copilots, models and agents running in your business, detects prompt, identity and data abuse, and responds by revoking tokens or ending sessions under policy — the production half of Aquila I’s Security for AI.
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Same software. Better outcome — at a lower cost.
How it’s rated
Full scoreboard ↓Quick answer
The Aquila I platform family
This page covers Aquila I AIS DR — detection and response for AI in production. The rest of the Aquila I platform:
Quick facts
30-second orientation- Product
- Detection and response for AI in production: SaaS AI, copilots, internal models and autonomous agents
- Names
- AI Systems Detection & Response; datasheet subtitle “AI Security & Data Risk”; some copy says AI-SDR
- Maker
- Aquila I Solutions Pvt. Ltd., Thane, Maharashtra; founded 2020; no outside funding reported (Tracxn)
- Price
- Quote-only; no published price, no INR list and no published licence meter
- Datasheet
- Dated January 2026; sold with AI Red Teaming as Aquila I’s Security for AI solution
- Response
- Policy-driven containment: access restriction, token revocation and session termination
- Visibility
- Whether it reads AI traffic by proxy, API connector, agent or browser is not documented
- MCP
- Aquila I says the architecture is built to extend discovery to MCP; not shown as shipped
- India
- Built in India, but the hosting region and where prompts are stored are not published
- In India via
- TechBag — AI inventory scoping, quote in INR with GST, a pilot on one business unit
Understand AI detection and response before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is AI detection and response?
Security that watches AI already in use, from copilots to agents, and acts when it is misused or leaking data.
An AI-use policy and a web proxy vs Aquila I AIS DR — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | An AI-use policy and a web proxy | Aquila I AIS DR |
|---|---|---|
| Staff use of AI tools | Discovered when someone mentions it | A live inventory of SaaS AI, copilots and agents |
| Data pasted into prompts | Unseen unless DLP inspects that app | Prompts and outputs inspected for sensitive data |
| Agent permissions | Granted once, never reviewed | Tokens and identities watched for abuse |
| Prompt injection | A developer’s problem, if anyone’s | Flagged as an interaction-layer detection |
| Response | A ticket to the application owner | Token revoked or session ended under policy |
| What it is NOT | — | A pre-release red team, or a product with a published price |
The cheapest test is one business unit: compare what discovery finds with your own AI list, then watch one copilot for a month.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Discovery
Continuous discovery of SaaS AI tools such as ChatGPT, Gemini and Copilot, internal models, fine-tuned LLMs and agents, with the identities and tokens that connect them.
Detection
Detections written for AI rather than log rules: prompt injection, guardrail bypass, goal abuse, agent intent drift, odd tool execution and identity misuse in AI access chains.
Response
High-risk behaviour triggers policy-bound actions, restricting access, revoking tokens or ending sessions, which Aquila I says fire by detection confidence and stay auditable.
Lakehouse
All AI security telemetry is normalised into Aquila I’s lakehouse for analytics and compliance reports, where AI SOC and AI Red Teaming findings can sit beside it.
Discover the AI estate, detect abuse in prompts, identities and agents, contain under policy — all telemetry in one lakehouse.
Nine capabilities. Discover, detect, contain.
Aquila I AIS DR watches AI after it goes live — who uses it, what data it touches and what its agents do.
SaaS AI and copilots found
Identifies third-party AI tools in use, ChatGPT, Gemini and Copilot among them, with usage patterns, data shared and access frequency.
Internal models and agents
Discovers internally deployed models, fine-tuned LLMs, autonomous agents and AI pipelines across cloud, workstations and production.
Identities and tokens mapped
Maps the service identities, tokens and delegated access that connect users and agents to AI systems, and flags excess permissions.
Injection and manipulation
Flags direct and indirect prompt injection, guardrail bypass attempts, goal abuse and interactions built to extract confidential data.
Sensitive data in prompts
Inspects prompts and generated outputs for confidential data leaking into or out of AI systems, and enforces policy on the interaction.
Agent and user baselines
Models normal AI usage per user, agent and system, then flags intent drift, unusual tool calls and abnormal access chains early.
Tokens revoked, sessions cut
Containment spans access restriction, token revocation, session termination and AI policy enforcement, inside limits you predefine.
Risk scored across the stack
Identity context, data sensitivity, prompt behaviour and agent activity are combined into a dynamic AI risk score for each system.
One view with AI SOC
Telemetry lands in the Aquila I lakehouse, so AI findings can be read beside AI SOC detections and AI Red Teaming results.
AI risk shows up in prompts, tokens and agents. Aquila I AIS DR watches all three in production.
Here’s what genuinely sets it apart — and exactly where it stops.
Built for AI that is already in production
Many AI security buys start with testing. AIS DR starts with what is live: the copilots staff use, the internal models and the agents acting on their own. Aquila I pitches behavioural and intent-based detection designed for AI rather than log rules, aimed at prompt abuse, data leaking through prompts and agents drifting off task.
Identities and tokens treated as the attack path
AI agents act through API keys, OAuth tokens and service identities. AIS DR watches those for excessive permissions and abnormal access chains, and its response options, restricting access, revoking a token or ending a session, act on the identity itself rather than only blocking a single prompt.
One platform with the SOC and the red team
AIS DR feeds the same lakehouse as Aquila I AI SOC, and AI Red Teaming findings add context about the system under attack. For a buyer already on Aquila I, that is one console and one vendor across discovery, testing and detection; bought alone, it is a young product from a small Indian company.
Where it stops
Aquila I does not document how AIS DR sees AI traffic, whether by proxy, API connector, endpoint agent or browser, so its coverage is unknown until you ask. MCP discovery is described as architecture built to extend, not a shipped feature. No price, hosting region, connector list or named customer is published.
The numbers behind the platform
What your Aquila I AIS DR rollout looks like
Inventory the AI you know about
List sanctioned AI tools, copilots, internal models and agents, who owns each, and which data classes they may touch.
Settle how it will see AI traffic
Get Aquila I to document the collection method, proxy, connector or agent, plus the hosting region and licence unit.
Run discovery on one business unit
Compare what AIS DR finds with your own list, then turn on prompt and data-exposure detection for one copilot.
Agree the response policy
Decide which detections may revoke a token or end a session on their own, and which only alert the SOC for review.
Wire it into the SOC and red team
Route AIS DR alerts to AI SOC or your SIEM, schedule AI Red Teaming for new releases, and sign a quote in INR.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Discovery listed AI tools our staff used that IT had never approved, with who used each one and how often.”
“A support copilot was being fed patient details in prompts; the data-exposure alert flagged it within the first week.”
“Revoking an over-permissioned agent’s token from the same screen as the alert is exactly what our SOC asked for.”
“Having AI alerts in the same lakehouse as our AI SOC cases saved us from building yet another integration.”
“We asked three times how it collects AI traffic before the deployment design was clear enough for our network team.”
“The hosting region was not on the datasheet, so our DPDP review stalled until Aquila I put it in writing for us.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the AI security market — tap any vendor to see why it sits where it does.
TechBag AI Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Quote-only; datasheet January 2026, no placement or case studies.
Workforce AI Coverage × Agent & Identity Depth
The grid nobody publishes — how much of your staff’s AI use a product sees vs how deep it goes into agents, tokens, identities and MCP.
Shadow AI plus token and session response; MCP is planned, not shipped.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Aquila I AIS DR vs the AI security field
Against the five AI security products in the table below, Palo Alto Prisma AIRS, Netskope, Trend Micro and iboss among them — on how each sees AI, shadow AI, agents, identity, response, price and India.
| Dimension | Aquila I AIS DR | Darktrace Secure AI | Palo Alto Prisma AIRS | Netskope One AI Security | Trend Vision One AI Security | iboss AI Security Platform |
|---|---|---|---|---|---|---|
| What it is | AI detection + response | Workforce and agent AI | Lifecycle AI platform | Four-product AI suite | Scanner, guard, access | Standalone AI control |
| How it sees AI | Not documented | Darktrace-run SaaS | Firewall or API call | Inline plus private AI | From Vision One | Endpoint agent |
| Workforce and shadow AI | SaaS AI + copilots | Discover and block | Discovery first step | 1,800+ AI apps inline | AI Secure Access | Free, risk-classed |
| Agents and MCP | Agents; MCP planned | Agent identities | Agent runtime checks | Agentic Broker | MCP governance | Not described |
| Threats detected | Injection, leaks, misuse | Jailbreaks, leaks, IPI | Seven threat types | ATLAS and OWASP | 9 of OWASP’s 10 | Data rules, paid |
| Identity and token abuse | Tokens and identities | Agents as identities | Not itemised | Account context | Identity-based rules | Per-user attribution |
| Response actions | Revoke, restrict, end | Block shadow AI | Block at runtime | Inline policy | AI Guard filters | Paid tiers only |
| Pre-release testing | AI Red Teaming, apart | Build-time risk only | AI Red Teaming module | AI Red Teaming product | AI Scanner | None |
| Pricing model | Quote; meter unknown | Quote; no meter shown | Monthly token volume | Quote only | Vision One credits | Free tier, then quote |
| Published entry price | Not published | Not published | No public rate | Not published | Free trial offered | Free visibility tier |
| India data location | Not published | US or EMEA only | India region since 2025 | Mumbai management plane | Not stated | AI data region unstated |
| Track record | Young, no case studies | GA September 2026 | Live since 2025 | GA March 2026 | Previewed late 2025 | Launched July 2026 |
| Lock-in and exit | Export terms unknown | Darktrace console | Region-bound keys | Traffic via Netskope | Platform credits | Uninstall the agent |
| Best fit | Aquila I platform buyers | Copilot-heavy workforces | Palo Alto estates | Netskope SSE users | Vision One customers | Free AI baseline |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Aquila I AIS DR if…
- ✓You already run, or are choosing, Aquila I AI SOC and want AI detections in the same lakehouse and console
- ✓Agents and copilots act through tokens and service identities, and you want to revoke or restrict them on detection
- ✓You prefer an Indian vendor and can test how it collects AI traffic during a pilot before you commit
Compare alternatives if…
- ✓Prompt data must be inspected in India today — Prisma AIRS has had an India API region since August 2025
- ✓You want to start free — iboss gives discovery, an AI inventory and prompt history at no cost
- ✓MCP control matters now — Netskope’s Agentic Broker and Trend’s AI Secure Access govern MCP traffic today
Do not expect…
- ✓A documented collection method, connector list or hosting region for AIS DR
- ✓Pre-release adversarial testing; that is Aquila I AI Red Teaming, quoted separately
- ✓A published price, an analyst placement or named customer case studies
TechBag has no AI security guide yet, so Aquila I AIS DR sits outside the category guides. Browse all products to compare it with the rest of the catalogue. →
What does policing AI use by hand cost you?
Drag the sliders (staff using AI tools; analyst-hour cost). Estimates model the security and compliance time spent finding unapproved AI use, reviewing what went into prompts and chasing over-permissioned agents, at an assumed 1.5 hours per AI user a year, with 70% of it removed by automated discovery and detection. Both figures are TechBag assumptions. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Quote only. Aquila I publishes no price for AIS DR, no INR list and no licence meter, so whether users, AI apps, agents or tokens are counted is the first question to ask. The collection method, hosting region and prompt retention are not published either. AI Red Teaming, the testing half of Aquila I’s Security for AI, is quoted separately. TechBag scopes your AI estate first, then gets the quote itemised in INR with GST.
Aquila I AIS DR
Best for AI already in production
- Quoted; no published licence meter
- Discovery, detection, token-level response
- Collection method and region to confirm
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ Aquila I AI Red Teaming
Best when you also build AI apps
- Tests LLM apps, RAG and agents pre-release
- Findings add context to AIS DR alerts
- Quoted as its own line
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
How does AIS DR see AI traffic — proxy, API connectors, endpoint agent or browser — and what does each method miss?
Which SaaS AI tools, model platforms and agent frameworks are supported today, by name, rather than on the roadmap?
Where are prompts, outputs and AI telemetry processed and stored, and will Aquila I commit that region in writing?
How long are captured prompts and outputs kept, who at Aquila I can read them, and can the period be shortened?
Which actions can run automatically, and how are token revocation and session termination approved and logged?
Is MCP discovery shipped today or still architecture “built to extend”? Ask for a demonstration on your own servers.
What does the quote count — users, AI apps, agents, tokens or prompts — and is AI Red Teaming a separate line?
Aquila I publishes no named customer case studies; can it arrange a reference call with a comparable Indian user?
Questions buyers ask
Ready to evaluate Aquila I AIS DR?
List the AI tools, copilots and agents you run first, or let a TechBag advisor get the collection method, region and licence unit in writing before a one-unit pilot.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.