If you work in India today, there’s a good chance your inbox is already part of someone’s playbook.
In the last couple of years, AI has quietly changed how cybercriminals work. Instead of clumsy, typo-heavy phishing emails, you now see perfectly written messages, in clean business English (or even local Indian languages), that reference real projects, colleagues, and vendors. Reports show that India is now one of the most targeted countries globally for malware and AI-driven attacks, with phishing and email compromise right at the center of the threat landscape.
At the same time, Indian organisations are being pushed to report incidents to CERT-In within six hours and to adopt “appropriate technical and organisational measures” under the emerging DPDP regime.
That means “we’ll deal with it if something goes wrong” is no longer a plan, you need to spot AI-powered attacks before they hit the inbox, not after someone clicks.
I’m saying this as someone who spends most of their time working with Indian teams, founders, CISOs, IT heads and building API-first email security that has to survive real-world Indian conditions: shared mailboxes, overworked IT, vendor-heavy workflows, and lean security budgets. What I’ve learned is simple:
The teams that win are not the ones with the fanciest dashboards. They’re the ones that design their people, processes, and tools to detect AI-powered attacks upstream before the user ever sees the email.
This article is my practical playbook for doing exactly that in India, across industries.
Why AI-Powered Attacks Feel “Invisible” to Your Teams
Traditional phishing had tells: bad grammar, weird email domains, clumsy logos.
AI-powered attacks don’t play by those rules:
- Attackers use generative models to write emails that sound like your actual regional sales head or your CA.
- They scrape LinkedIn, public filings, vendor websites, and social media to personalise messages at scale.
- They can iterate thousands of variants of the same email until they find the one that gets past your filters.
From my conversations with Indian teams, these are the three reasons these attacks are slipping through:
Defences still think in “signatures” and “keywords." Many organisations still treat email security as a spam problem. If there’s no obviously malicious link, no keyword like “urgent payment”, and the sender domain isn’t on a blocklist, the email sails through.
Human training is stuck in the 2017 phishing world. A lot of awareness sessions in India still use outdated examples, broken English, Nigerian prince–style scams, childish logos. Meanwhile, AI-generated phishing emails are clean, on-brand, and often built around real internal workflows.
Most teams only see the attack after the click. Escalation workflows kick in after someone in finance reports “this payment looks odd” or the SOC notices weird logins. By then, your CERT-In six-hour reporting clock may already be ticking.
To flip this, you need to design for pre-inbox detection combining AI-driven controls with disciplined processes and realistic training.
The New Reality: India Is in an AI Cyber Arms Race
Indian businesses are in a uniquely intense spot:
- India was recently reported as the most targeted country globally for malware attacks, with AI-driven phishing and ransomware playing a major role.
- Reports show billions of phishing emails sent every day worldwide, with AI-generated variants rising sharply after the mainstream adoption of large language models.
- Local analyses point out that Indian SMBs, in particular, are facing a surge in AI-powered phishing and social engineering, often delivered through email, SMS, WhatsApp, and collaboration tools.
Layer on top of this:
CERT-In’s six-hour reporting mandate for cyber incidents.
The DPDP Act and Rules, which emphasise appropriate security controls, breach notification, and accountability for data fiduciaries.
You’re not just trying to avoid a scam anymore. You’re trying to avoid regulatory exposure, reputational damage, and days of operational disruption.
All of that starts with better detection, earlier and smarter.
How AI Email Security Should Actually Work (Not Just in Vendor Slides)
When we started working with Indian teams on modern email security, one thing became obvious: if your stack doesn’t understand context, it will lose to AI-generated attacks.
A robust, pre-inbox AI defence should do at least five things well:
1. Behaviour and Relationship Baselines
Instead of asking, “Is this email on a known bad list?”, your system should be asking:
- Does this sender usually talk to this recipient?
- At this time of day?
- About this kind of topic?
- From this geography, device, or IP range?
AI-driven models can learn normal patterns across your org who usually signs POs, how your finance team exchanges approvals, which suppliers send what kind of attachments and then flag unusual combinations even when the content looks perfect.
2. Deep Language and Intent Analysis
AI should read the email like a sharp analyst:
- What is the email trying to get the user to do? Log in, change bank details, pay an invoice, share OTPs?
- Is the tone subtly urgent, manipulative, or guilt-inducing (“just do this quickly”, “we will miss the deadline”)?
- Is the language aligned with prior conversations, or does it feel like a fresh narrative injected into an old thread?
Modern AI engines can detect intent especially when messages push for financial actions or credential harvesting that don’t fit normal workflow.
3. Multi-Signal Correlation Across Channels
Attackers don’t think in “email only” anymore:
- They may send a WhatsApp or Teams message (“Just approved that invoice, check the mail”) followed by the email.
- They may use lookalike domains, redirects through legitimate services, or newly registered sites targeting Indian brands.
Your defence stack should correlate:
- Domain age and reputation
- URL redirection chains
- Similar attack templates seen globally
- User behaviour after clicking links
This is where cloud-native, AI-powered tools are strong: they learn from global campaigns and apply that learning to your mailboxes within seconds.
4. Pre-Campaign and Pre-Inbox Detection
The strongest teams I work with don’t wait for the first victim email. They:
- Monitor for suspicious domain registrations mimicking their brand or their key partners.
- Run threat intelligence feeds to spot new infrastructure used in campaigns before those messages reach users.
In practice, this looks like:
- Blocking connections to known malicious domains at the browser level.
- Quarantining any email pointing to those domains, even if the content looks innocent.
- Think of it as watching the attackers set up the stage and shutting off the power before the show starts.
5. API-First, MX-Agnostic Architecture
A practical Indian reality: you probably use Microsoft 365 or Google Workspace, and you don’t want to break everything by changing MX records.
Modern AI email defences plug in via APIs:
- They read and score messages in real time.
- They don’t force mail flow changes that your IT team will be afraid to touch on a Friday evening.
This is the route we chose deliberately, it’s the only way I’ve seen mid-sized Indian organisations actually adopt advanced controls without year-long migration projects.
What “Before It Hits the Inbox” Looks Like in Different Indian Industries
Different sectors see different flavours of AI-powered attacks. The patterns repeat, but with local colour.
1. BFSI and Fintech
Attack pattern: Hyper-realistic payment requests, fake RBI or SEBI notices, loan offer scams, deepfake emails from senior leadership.
Pre-inbox signals to use:
- Strict DMARC/DKIM/SPF for all finance-related domains.
- Higher-sensitivity anomaly detection for finance group mailboxes.
- Auto-quarantine of emails that attempt bank detail changes, even if apparently from known vendors.
2. IT / SaaS / Startups
Attack pattern: Compromised collaboration tools, fake access requests, “security update” prompts claiming to be from cloud vendors.
Pre-inbox signals:
- Extra scrutiny on emails that mix product, infra, and access language (“GitHub”, “IAM”, “S3”, “VPN”).
- Correlation between suspicious sign-in attempts and new email threads.
- Isolation of links to login pages until sandboxed.
This is where Indian SaaS teams often underestimate risk, they assume “we’re the tech people, our folks know better.” AI-powered phishing doesn’t care.
3. Manufacturing & Supply Chain
Attack pattern: Fake logistics updates, changed bank details for suppliers, fraudulent invoices, impersonation of customs or transport partners.
Pre-inbox signals:
- Tight vendor domain allowlists for invoicing and shipment communication.
- Auto-flags when bank account details are mentioned in an email thread that historically never discussed payments.
- Correlation with newly registered domains mimicking logistics brands.
4. Healthcare & Pharma
Attack pattern: Fake patient record requests, insurance settlement emails, regulatory “inspections” demanding login access.
Pre-inbox signals:
- Stricter checks on emails with attachments claiming to be reports, prescriptions, or lab results.
- Language models tuned to spot unusual “urgency” in patient or regulator communications.
5. Education, Government & Non-profits
Attack pattern: Fake grant approvals, exam result notifications, scholarship scams, impersonation of ministries or state agencies.
Pre-inbox signals:
- Strong verification for emails claiming to be from gov.in or nic.in, including subdomain analysis.
- Blocks for mass-mail patterns that don’t match established campaign senders.
When we map attacks this way with customers, teams suddenly see patterns instead of random accidents. And once people see patterns, they can design controls that operate before users ever see the message.
A Practical 5-Step Playbook for Indian Teams
Here’s the approach I recommend when we start with a new organisation in India, regardless of industry.
Step 1: Map Your “High-Risk Conversations”
List the workflows where a single email can move real money or data:
- Vendor payment approvals.
- Salary or reimbursement changes.
- Credential resets and MFA bypasses.
- Access grants to core systems.
- Customer data exports or report sharing.
These become your Tier-1 protected flows. Any email that tries to drive one of these actions should face stricter pre-inbox checks.
Step 2: Turn On AI-First Detection Where It Actually Matters
Instead of trying to protect every mailbox equally from day one:
- Start with finance, HR, CXO, procurement, IT admin, and key project teams.
- Integrate an AI-driven email defence via API to monitor and score messages to these groups.
- Configure it to quarantine high-risk messages and soft-warn medium-risk messages (banners, in-line prompts) before delivery.
Step 3: Feed Back Real Incidents Into the Model and Training
Every incident even near misses is a learning asset.
In my own work, we ask three questions for every serious attempt we see:
- What signal did we already have that could have caught this earlier?
- Which email pattern or domain should now be treated as “pre-inbox block”?
- How do we turn this into a 5-minute scenario for staff training?
This transforms detection from a static product into a living, organisation-specific defence system.
Step 4: Align With CERT-In and DPDP From Day One
Because of the six-hour reporting window, your detection and incident response need to be joined at the hip:
- When a suspicious email is auto-quarantined, it should create a traceable event in your incident log.
- If it turns out to be a real incident, your legal/compliance teams should have enough data to decide whether CERT-In reporting and DPDP breach notifications are triggered.
- Teams that design email security with this regulatory context in mind sleep better. You’re not scrambling after the fact.
Step 5: Measure What Actually Matters
Instead of vanity metrics like “number of emails scanned”, focus on:
- Reduction in phishing emails reaching users’ primary inboxes.
- Time from malicious email creation to block/quarantine.
- Click-through rates on high-risk messages that did reach users.
- Mean time to detect (MTTD) and respond (MTTR) to email-origin breaches.
When we do quarterly reviews with customers, these are the numbers that tell the real story, not how many rules were added.
A Simple “Pre-Inbox Red Flag” Cheat Sheet for Your Teams
Even with strong AI controls, teams should understand what upstream detection is looking for.
You can adapt a table like this into your internal wiki or onboarding deck:
The point is not to turn employees into SOC analysts. It’s to give them language for what your AI and security stack are already doing silently in the background.
From “Inbox Victims” to “AI-Aware Teams”: My Closing Thoughts
When I started working closely with Indian organisations on email security, I saw the same pattern over and over:
- Everyone thought phishing was a user training problem.
- Nobody realised how far attackers had gone with AI – voice clones, deepfakes, multi-channel scams.
- Most tools still treated email like it was 2010: blacklists, signatures, and static rules.
- The teams that are now ahead of the curve did three things differently:
- They accepted that AI is both the attacker and the defender.
- They stopped relying only on human vigilance and classic filters, and invested in AI that can read, reason, and correlate like an analyst but at machine speed.
- They designed controls around their critical business flows, not generic best practices.
- They didn’t roll out every feature everywhere on day one. They protected payment approvals, access changes, and data exports first.
- They fused technology with India’s regulatory reality.
CERT-In reporting and DPDP obligations weren’t afterthoughts; they were part of how they chose tools, structured logs, and built playbooks.
If you’re reading this in India, whether you’re in BFSI, SaaS, manufacturing, healthcare, education, or the public sector, the question is no longer “Will AI-powered attacks reach us?”
They already have.
The real question is:
Will your teams spot AI-powered attacks before they hit the inbox or only after someone has clicked?
If you start by mapping your high-risk workflows, bring in AI-driven, API-first email security, and align everything with India’s incident and data protection rules, you’ll be far ahead of most of the market.
And in this new inbox battleground, “a little bit ahead” is often the difference between a routine security review and a front-page incident.
