Enterprise AI Security · AlgoProfessor AI R&D Solutions

Prove every model you ship.

We build the assurance layer for enterprise machine learning. Every dataset, checkpoint and fine-tune gets a signed lineage record. Every release is scanned for trojans with a stated false-alarm rate. Every erasure claim is audited, and every decision lands in a tamper-evident log your auditors can verify.

Book a supply-chain assessment See how the gate works

Plugs into MLflow registries Mapped to EU AI Act · NIST AI RMF · ISO/IEC 42001 Hyderabad, India

release-gate · PEP 1 Simulated

Log size1,284,553

Log head9f2c41e07b…

Policyv1 · tiered

L lineage S trojan scan E erasure audit digests: real SHA-256, computed in your browser

Site pulse

Who is reading about AI supply-chain security

Connecting to the visitor counter…

Total visits—

Unique visitors—

Visits today—

Your models pass through more hands than your code review ever sees.

A production model is assembled from shared datasets, a base model downloaded from a public hub, training that may run at a vendor, and edits made after release. Each hand-off is a place where something can be swapped, poisoned or quietly left undone.

Data sourcing

Shared, versioned datasets feed dozens of models.

A1 poisoned samples, hidden triggers

Training

Internal clusters, external vendors, fine-tunes of fine-tunes.

A1 insider trains in a backdoor

Model registry

Versions, aliases and artifacts in shared storage.

A2 weights edited after sign-off

Post-release edits

Fine-tuning and erasure requests under GDPR.

A3 "the data was erased" when it wasn't

Deployment

Containers admitted to the serving fleet.

A2 · A4 substitution, stolen or revoked keys

How often will a clean model be blocked?

A detector with an unknown false-alarm rate stalls delivery until teams route around it. Our gate states its false-flag rate before you adopt it.

Calibration results →

Which models are affected if one dataset turns out to be poisoned?

Without a lineage graph this is a week of spreadsheets. With signed lineage it is a query that returns in milliseconds.

How lineage works →

Can you show that an erasure request was honoured?

We test the model against retrained references with an exact error guarantee, and fall back to attested retraining when behaviour alone cannot tell.

Erasure audits →

Can an auditor check a release decision a year from now?

Every decision is signed and appended to an append-only log with inclusion and consistency proofs. Rewriting history breaks the proofs.

Evidence for regulators →

The platform

An assurance control plane that sits beside the MLOps stack you already run.

We intercept your pipeline at exactly two points: when a model version is promoted in the registry, and when a container is admitted to serving. Everything else stays as it is.

Your data planeExisting MLOps

Data lake · feature storeTraining clusters & vendorsExternal model hubsModel registryPEP 1 · promotionCI/CD releaseServing fleetPEP 2 · admission

We addAssurance control plane

Lineage serviceInspection workersUnlearning auditsPolicy decision pointPolicy-as-code tiersTransparency log

Shared with securityTrust & governance

KMS / HSM team keys & rolesEvidence storeCompliance reportingIncident recall queries

Release workflow at PEP 1Step 1 of 8

Select a step

STEP 1

A team asks to promote a model version

The CI pipeline requests promotion of version v to release-candidate. The registry holds the request at PEP 1 until a decision comes back.

registry · promotion hook

STEP 2

The gate gathers evidence

PEP 1 sends the version, its artifact digest and its risk tier to the policy decision point.

tier: minimal · limited · high

STEP 3

Lineage is verified back to registered roots

Every signed link is checked: valid signature, key not revoked, the signer's role allowed to perform that step, and every input itself attested. A substituted artifact or out-of-role signature fails here and always blocks.

Ed25519 links · Merkle commitments

STEP 4

The model is scanned for trojans

Detectors that proved themselves on your own shadow models are fused into one conformal p-value, calibrated against clean models from the same pipeline. That calibration is what bounds the false-flag rate.

trigger reconstruction · meta-classifier

STEP 5

Erasure claims are audited

If the version claims to have forgotten data, it is tested against models retrained without that data. Class-level erasure gets a functional test at no extra training cost.

full-conformal · reference bank · functional

STEP 6

The tier's policy is applied

Policies are versioned code. Integrity failures block. Inspection flags block or go to human review depending on the tier, with the reconstructed trigger attached for the reviewer.

policy-as-code v1

STEP 7

A signed certificate is logged

The decision, evidence digests, p-values, thresholds and policy version are signed and appended to the transparency log, which returns an index and an inclusion proof.

RFC 9162-style log

STEP 8

Release, review or block

The certificate travels with the container. At PEP 2 the serving fleet re-checks the digest and the proof, so anything deployed without a log entry is an alert in itself.

admission control · PEP 2

Try it

Why a single flipped bit cannot hide

Each weight shard is hashed into a Merkle tree, and the root is signed at training time. Click a shard to see the handful of hashes needed to prove it belongs. Then flip one bit and watch the root stop matching.

In our tests, 98.6% of random single-bit weight flips left test accuracy within half a point, so behavioural checks would miss them. Cryptographic commitment caught all of them.

weights.npz · 8 shards · SHA-256 with RFC 6962 prefixes

Attested root (signed at training)…

Recomputed root…

Select a shard to see its inclusion proof.

Operations dashboard

What your security team sees every day

The assurance console gives security, ML platform and audit teams one live view of every release decision, the review queue, fleet risk and detector coverage. This demo runs on a simulated release stream; the detector and calibration panels show our measured research results.

Assurance consoledemo tenant · policy v1Simulated data

--:--:--

Business unit

Risk tier

Time range

Releases evaluated0in range

Released0%passed every check

Sent to review0inspection flags

Blocked0integrity & erasure

Open reviews now0awaiting a reviewer

Transparency log0certificates, append-only

Release decisions over time

per 10 s

ReleasedReviewBlocked

Alert feed

newest first

Open reviews

flags awaiting triage

Models in production

by unit and tier

MinimalLimitedHigh risk

Gate calibration

measured

False-flag rateTrojans caughtBound (= α)

Detector coverage by attack family

measured · Fashion-MNIST MLP zoo · TPR at α = 0.10

0%100%Darker cells: fewer trojans caught. Gate = admitted detectors fused with conformal p-values.

Services

What we deliver

Start with an assessment, deploy the gate, or bring us in for one control. Every engagement ends with artifacts your security and audit teams keep: code, calibration data, runbooks and evidence.

Start heretypically 2–3 weeks

AI Supply-Chain Security Assessment

We map your model estate end to end: where data comes from, which base models you depend on, who trains what, how artifacts move into production, and which of those steps leave no verifiable record. You get a threat model and a prioritised plan.

Lineage map of datasets, base models, fine-tunes and deployments
Threat model covering poisoning, tampering, false erasure claims and key misuse
Gap report against EU AI Act Arts. 10, 12 and 15, NIST AI RMF and ISO/IEC 42001
Recommended risk tiers and gate thresholds for your release volume
Executive briefing and a costed roadmap

NewQuantum-safeassessment 3–4 weeks

Post-Quantum Cryptography Migration

We find every place your AI and data platforms use RSA, elliptic curves or short keys, rank them by how long the data or signature must stay secure, and plan the move to NIST's post-quantum standards with hybrid modes that keep today's systems working.

Cryptographic inventory and bill of materials (CBOM) across TLS, PKI, code and model signing
Hybrid ML-KEM key exchange and ML-DSA / SLH-DSA signatures (FIPS 203, 204, 205)
Roadmap aligned to NIST IR 8547 (draft), CNSA 2.0 and the EU PQC roadmap

For: CISOs, PKI owners and teams holding long-lived sensitive data

NewGenerative AI2–4 weeks

GenAI & LLM Application Security

Security reviews and guardrails for chatbots, copilots and retrieval-augmented generation: what the model can be tricked into saying, revealing or retrieving, and what happens when your documents are poisoned.

Prompt-injection and jailbreak testing, direct and through retrieved content
Sensitive-data leakage and RAG poisoning assessment
Controls mapped to the OWASP Top 10 for LLM Applications

For: product teams shipping LLM features to customers or staff

NewAgentic AI2–4 weeks

Agentic AI Security

AI agents act with real permissions: they call tools, run code and move data. We review what each agent can do, which tools and plugins it trusts, and how a hijacked goal or a poisoned tool would play out.

Tool and permission review with least-privilege agent identities
Supply-chain checks for agent tools, plugins and MCP servers
Threats mapped to the OWASP Top 10 for Agentic Applications (2026)

For: teams deploying autonomous or semi-autonomous agents

NewOffensive testing1–3 weeks per cycle

AI Red Teaming

Adversarial testing of your models and AI products before attackers do it: backdoors, evasion, data extraction and misuse, reported with reproducible test cases and fixes.

Structured campaigns using MITRE ATLAS tactics
Automated probing with open tools such as garak and PyRIT, plus manual expert testing
Retests after fixes and regression suites for your release gate

For: high-risk AI systems before launch and after major changes

Registry · Deployment3–6 weeks

Assurance Gate Deployment

We install the two enforcement points in your stack: promotion in the model registry and admission at serving. Policy tiers are written as versioned code you own.

PEP 1 and PEP 2 integration (MLflow first; other registries on request)
Policy decision point with minimal, limited and high-risk tiers
Signed release certificates and admission checks

For: platform and MLOps teams shipping models every week

Data · Training · Registry2–4 weeks

Signed Lineage & Transparency Log

Every dataset, configuration and checkpoint is committed with Merkle trees, and every pipeline step is signed by a team key with a role. Release decisions go to an append-only log.

Role-authorised signing with KMS/HSM key management and revocation
Dataset and base-model recall queries for incident response
Inclusion and consistency proofs for auditors

For: security, audit and incident-response teams

Modelsongoing or per release

Calibrated Trojan Inspection

We scan model versions for backdoors with detectors that first prove themselves on shadow models trained in your own pipeline. Scores become p-values, so every tier has a known false-flag rate.

Detector admission study on your architectures
Calibration population per pipeline, refreshed when it changes
Reviewer packs with the reconstructed trigger and suspected target label

For: teams using third-party data, vendors or public base models

Post-releaseper request or as a service

Machine-Unlearning & Erasure Audits

When a model is said to have forgotten data, we test the claim against models retrained without it. You get a verdict with a stated error rate, or a recommendation to rely on attested retraining.

Full-conformal audit with an exact finite-sample guarantee
Pre-built reference banks for fast screening of routine requests
Functional test for class-level erasure

For: data-protection officers and teams handling GDPR Art. 17 requests

Governance2–3 weeks

Compliance Evidence Pack

We connect the controls above to the obligations you report on, and generate the documentation from the lineage graph and the log, so evidence stays current without manual collection.

Control-to-obligation mapping for EU AI Act, NIST AI RMF, ISO/IEC 42001
Technical documentation and record-keeping exports
Post-market monitoring and recall procedures

For: risk, compliance and internal audit

Leadership · R&Dretainer

AI Security Innovation Centre Advisory

For organisations building their own AI security function or Technology & Innovation Centre: research roadmap, platform architecture, benchmarking standards and publication strategy.

Multi-year research and technology roadmap
Standards for security claims, benchmarks and reproducibility
Mentoring for research, engineering and MLOps teams; IP and publication support

For: CTOs, CISOs and heads of AI research

People2–5 days

AI Security Training for Teams

Hands-on workshops for engineers, security analysts and reviewers on how ML supply-chain attacks work and how to operate the gate day to day.

Backdoor attacks and detection labs
Reading release certificates and triaging inspection flags
Running erasure audits and recall drills

For: engineering, security operations and review teams

Quantum-safe security

Records you sign today must still be trusted when quantum computers arrive

A large quantum computer running Shor's algorithm would break RSA, elliptic-curve key exchange and today's signatures, including the Ed25519 signatures on model lineage. Attackers can record encrypted traffic now and decrypt it later, and a model attestation signed today may need to hold up in an audit ten years from now. We help you inventory your cryptography and move to the NIST post-quantum standards before that window closes.

Harvest now, decrypt later

Encrypted training data, model weights in transit and API traffic captured today can be decrypted once a quantum computer exists. Data with a long confidentiality life is already exposed.

Sign now, forge later

Lineage signatures, release certificates and code-signing keys that must stay verifiable for years need quantum-resistant or hybrid signatures before classical ones can be forged.

What stays safe

Hash-based commitments survive. SHA-256 Merkle trees and transparency logs keep a large security margin against quantum search, so our lineage commitments carry over; only the signatures need upgrading.

The migration clock

filled: already happened · outline: upcoming

Aug 2024NIST publishes FIPS 203, 204, 205ML-KEM, ML-DSA and SLH-DSA become final standards
Mar 2025HQC selectedBackup key-encapsulation algorithm chosen; FN-DSA (FIPS 206) follows in draft
Apr 2025OpenSSL 3.5 LTSML-KEM, ML-DSA and SLH-DSA reach mainstream TLS libraries
Jun 2025EU coordinated roadmapMember states asked to start inventories and pilots
End 2026EU first steps dueNational strategies, cryptographic inventories, pilots
2027CNSA 2.0 for new systemsUS national-security acquisitions must support quantum-resistant algorithms
2030RSA / ECC deprecatedNIST IR 8547 draft; EU high-risk critical systems migrated
2035Quantum-vulnerable crypto disallowedNIST draft target; CNSA 2.0 complete; EU medium-risk systems migrated

Where your AI stack uses vulnerable cryptography

Today	Used for	Quantum risk	Move to
RSA, ECDH key exchange	TLS to model APIs, registries, data lakes	Broken by Shor	ML-KEM (FIPS 203), hybrid X25519 + ML-KEM-768
RSA, ECDSA, Ed25519 signatures	Lineage links, release certificates, code and model signing	Broken by Shor	ML-DSA (FIPS 204), hybrid Ed25519 + ML-DSA during transition
Long-lived root keys	Transparency-log roots, PKI roots	Broken by Shor	SLH-DSA (FIPS 205), hash-based and conservative
AES-128	Weights and datasets at rest	Margin reduced	AES-256
SHA-256 Merkle trees	Dataset and checkpoint commitments, logs	Remains secure	Keep; SHA-384 where policy asks for extra margin

Is your data already at risk?

Mosca's rule: if the years your data or signatures must stay secure plus the years your migration takes exceed the years until a cryptographically relevant quantum computer, you are already late.

Must stay secure for 10 years

Your migration takes 5 years

Years until a quantum threat (your estimate) 12 years

Expert estimates for the quantum threat vary widely. Use the range your risk committee accepts.

Expertise & tools

The disciplines and the stack behind each engagement

AI supply-chain security sits where machine learning, cryptography, statistics and MLOps meet. These are the areas we work in, and the tools we use or integrate with. We mark clearly which ones run in our own prototype today.

Models

Backdoor & trojan detection

Trigger reconstruction, neuron stimulation, meta-classifiers trained on shadow models, and spectral forensics to find poisoned samples.

research + prototype

Post-release

Machine unlearning & privacy audits

Exact unlearning by sharded retraining, calibrated membership inference, and full-conformal tests for erasure claims.

research + prototype

Integrity

ML supply-chain cryptography

Merkle commitments for datasets and checkpoints, role-authorised Ed25519 signing, and append-only transparency logs with inclusion and consistency proofs.

research + prototype

Statistics

Statistical assurance

Conformal calibration with finite-sample error bounds, bootstrap confidence intervals, error budgets across detectors, and base-rate planning for review load.

research + prototype

Platform

MLOps integration

Registry promotion hooks, deployment admission control, policy-as-code tiers and evidence stores that fit the pipelines your teams already run.

MLflow integrated; others scoped per client

Governance

AI regulation & standards

Evidence mapped to the EU AI Act, GDPR Art. 17, NIST AI RMF and ISO/IEC 42001, plus industrial standards such as SLSA and ML bills of materials.

mapping and documentation

Quantum-safe

Post-quantum cryptography

ML-KEM, ML-DSA and SLH-DSA (FIPS 203–205), hybrid key exchange and signatures, cryptographic inventories and migration planning.

advisory and migration planning

GenAI & agents

LLM and agentic AI security

Prompt injection, RAG poisoning, tool and plugin supply chains, agent permissions and AI red teaming against OWASP and MITRE ATLAS.

assessment and testing

Tools and where they apply

Tool or method	Status	Data	Training	Registry	Post-release	Deploy	Governance

primary usesupporting usePrototype runs in our research prototype todayIntegration supported targets for client deployments, confirmed during scoping

Evidence

Numbers from our research prototype

The methods behind our services come from AlgoProfessor's own research on enterprise AI supply-chain assurance. These are measurements, with the setting stated, so you can judge what transfers to your systems.

100,000

models in one registry

402,200 signed lineage links verified in 45.7 s; a single model's cold check took about 1 ms at every size.

15.2s

to commit a 14 GB checkpoint

A file the size of a 7B-parameter fp16 model, streamed from disk in 4 MiB shards; proofs need 12 hashes.

12/ 12

tampered artifacts blocked in MLflow

Ten of twelve trojaned versions were routed to review and 34 of 36 clean versions released, in a live MLflow registry.

9.6% at α = 10%

false-flag rate, as promised

Across 2,000 calibration re-splits the gate's false-flag rate tracked its nominal level from 5% to 30%.

6.7% vs 30%

false rejection of honest unlearning

Our full-conformal erasure audit against the per-example test it replaced, while still catching every model that never unlearned.

0.903AUC

fused detector gate

Across five attack families on Fashion-MNIST networks; better than either admitted detector alone (0.735 and 0.772).

Setting: research prototype on a 2-vCPU cloud machine; detection on Fashion-MNIST MLPs and CNNs; registries above 60 versions are synthetic. Your models will behave differently, which is why every engagement starts by calibrating on your own pipeline. Manuscript in preparation.

What we learned

One detector is never enough

An adaptive attack that planted several small triggers drove trigger reconstruction to an AUC of 0.00. A meta-classifier was unaffected. Our gate fuses detectors that earn admission on your shadow models.

What we learned

Shadow models must look like real threats

On convolutional networks, a detector that looked strong on simple shadow backdoors generalised poorly to real attacks. We now build shadow sets as diverse as the threats you care about.

What we learned

Some unlearning can't be audited from outside

When an approximate method erased a class so completely that it behaved like a retrain, no black-box test could tell them apart. For those cases we attest the retraining run itself.

Plan your review load

What would the gate flag in your release stream?

Model releases per month 10,000

Share of releases that are trojaned (assumption)

Gate level α (tier)

Reviewer minutes per flag 30

Clean models flagged1,002sent to review, not blocked

Trojans caught69.031.0 missed

Precision of a flag6.4%share of flags that are real

Review effort535 h≈ 3.3 reviewers full time

real trojans among flagsclean models among flags

At realistic prevalence most flags are false alarms. Route inspection flags to review, and block automatically only on integrity failures.

Detection and false-flag rates are the measured curve of our Fashion-MNIST MLP gate. Release volume and prevalence are your assumptions.

Case studies

Five problems we have worked through in the lab

Each study comes from experiments on AlgoProfessor's research prototype, with real models, real attacks and real measurements. They are lab studies, not client engagements, and each states its setting so you can judge how it maps to your estate.

Lab study · MLflow 3.17 · 60 model versions

Stopping tampered weights inside a live model registry

Problem: Trained artifacts sat in shared storage where they could be edited after sign-off, and some versions had been trained on poisoned data.
What we did: Wrapped MLflow's alias promotion (PEP 1) and production lookup (PEP 2) with the assurance gate, then registered 60 versions of a classifier: 36 clean, 12 trojaned, and 12 whose weights were edited after the training step signed them.
Outcome: Every tampered artifact was blocked at the integrity check. Ten of twelve trojaned versions went to human review. Thirty-four of 36 clean versions were released. The gate added about 154 ms per promotion, excluding the asynchronous scan.

12/12tampered blocked

10/12trojans to review

154 msgate overhead

Gate decisions by model category (count of versions)

ReleasedSent to reviewBlocked

View data

Category	Released	Review	Blocked
Clean (36)	34	2	0
Trojaned (12)	2	10	0
Tampered (12)	0	0	12

Lab study · UCI digits & Fashion-MNIST · 90 erasure requests

Checking a GDPR erasure claim with a known error rate

Problem: The usual way to test unlearning compares losses on erased and unseen data. On well-trained models it cannot tell anything apart, and a stricter per-example test rejected honest retraining far too often.
What we did: Built a full-conformal audit against 24 models retrained without the erased data, plus a reusable bank of exact retrains that needs no training per request. We tested both against 540 audited models.
Outcome: False rejection of honest retraining fell from 30% to 6.7% on digits and from 27% to 0% on Fashion-MNIST. Every model that never unlearned was still caught.

6.7%false rejection (was 30%)

100%never-unlearned caught

0trainings per request (bank)

False rejection of honest (retrained) models (% of audits, random 10% erasure; dashed line: target 5%)

Previous per-example testFull-conformal auditReference bank

View data

Dataset	Per-example (v1)	Full-conformal	Bank
UCI digits	30.0%	6.7%	6.0%
Fashion-MNIST	26.7%	0.0%	10.7%

Lab study · 2-vCPU machine · registries of 1,000 to 100,000 models

Verifying every model in a 100,000-model registry

Problem: Provenance checks that work for a few models often stall at fleet scale, and a 70-billion-parameter checkpoint is hundreds of gigabytes.
What we did: Verified the full signed lineage of registries with 1,000, 10,000 and 100,000 models, timed single-model checks at each size, and committed a real 14 GB file streamed from disk.
Outcome: Full verification grew in step with the registry, reaching 45.7 s for 100,000 models. Checking one model stayed at about 1 ms at every size, and the 14 GB file was committed in 15.2 s.

45.7 s100k-model verify

≈1 msper-model check

15.2 s14 GB commit

Full-registry verification time (seconds, log scale)

View data

Models	Signed links	Full verify	Single-model check (median)
1,000	4,025	0.64 s	1.01 ms
10,000	40,220	4.53 s	1.09 ms
100,000	402,200	45.68 s	1.00 ms

How an engagement runs

From first workshop to a gate your teams operate

A typical deployment for one business unit. Timelines depend on how many pipelines and registries are in scope.

PHASE 1

Discover

weeks 1–2

Interviews, pipeline walk-throughs and a lineage map of your estate.

Threat model
Regulatory gap report
Tier proposal

PHASE 2

Integrate

weeks 3–6

Signing keys, lineage commitments and the two enforcement points go in.

PEP 1 and PEP 2 live
Transparency log
Policy-as-code repo

PHASE 3

Calibrate

weeks 7–8

Shadow and calibration models trained in your pipeline; detectors admitted on evidence.

Measured false-flag rates per tier
Erasure reference banks
Review runbook

PHASE 4

Operate

ongoing

Your teams run the gate; we monitor calibration drift and refresh detectors.

Quarterly calibration review
Recall drills
Compliance exports

Compliance

Evidence your regulators and auditors can check

Each control produces records that map to the obligations below. We help you assemble them; conformity assessment and legal sign-off remain with you and your advisers.

Obligation	Platform control	Evidence produced
EU AI ActArt. 10 · data and data governance	Dataset commitments, signed ingest links	Merkle roots of every training set; lineage from each model to its data
EU AI ActArt. 11, Annex IV · technical documentation	Signed release certificates	Evidence digests, p-values, thresholds and policy version for every release
EU AI ActArt. 12 · record-keeping	Transparency log	Append-only record with inclusion and consistency proofs
EU AI ActArt. 15 · robustness and cybersecurity	Trojan inspection, integrity checks	Calibrated inspection results; tamper detection and localisation
EU AI ActArt. 17 · quality management	Versioned policy-as-code	Policy history; each decision bound to the version in force
EU AI ActArt. 72 · post-market monitoring	Recall and key-compromise queries	Every downstream model affected by an incident, within milliseconds
GDPRArt. 17 · right to erasure	Unlearning audits, attested retraining	Per-request verdict with p-values, or signed retraining lineage
NISTAI RMF · Govern, Map, Measure, Manage	Tiers and roles, lineage graph, calibrated tests, release and recall	Policy documents, dependency graph, error-rated test results, decision records
ISO/IEC42001 · AI management system	The platform end to end	Documented, auditable lifecycle controls
NISTFIPS 203, 204, 205 · IR 8547 (draft) · NSACNSA 2.0	Post-quantum migration, hybrid signatures	Cryptographic inventory (CBOM), migration plan, quantum-safe certificates and signatures
OWASPTop 10 for LLM Applications · Top 10 for Agentic Applications	GenAI and agentic security reviews, AI red teaming	Test reports with reproducible cases, fixes and retest results

This mapping describes the evidence each control produces. It is not legal advice and has not been reviewed by a conformity-assessment body.

About

Research-led AI security from Hyderabad

AlgoProfessor AI R&D Solutions builds and teaches applied AI, from agentic systems to quantitative finance and research training. Our AI security practice turns current research on backdoors, unlearning and supply-chain integrity into controls an enterprise can run.

Our research An Enterprise-Scale Assurance Platform for AI Supply-Chain Security: Calibrated Model Inspection, Verifiable Unlearning Audits, and Transparent Lineage S. Satyanarayana · AlgoProfessor AI R&D Solutions · manuscript in preparation

Dr. S. Satyanarayana

Ph.D., PDF (AI) · Founder, CEO & Chief Agentic AI Scientist

60+SCI, Scopus and IEEE publications

11+patents

11books

56Ph.D. and postdoctoral scholars

International Postdoctoral Research Co-Supervisor, NYUST (YunTech) and NKUST Research Institute of IoT Cybersecurity, Taiwan
Editor-in-Chief, IJCMI

Questions teams ask

Do you need access to our training data or model weights?

For lineage and the gate, no: we work with digests and signed metadata, and everything runs inside your environment. Trojan scans and erasure audits run where your models live, under your access controls, and the results leave as signed certificates.

Which registries and platforms do you support?

Our reference integration is MLflow. The two enforcement points are narrow hooks, so other registries and admission controllers can be added during the Integrate phase; we scope that in the assessment.

Will the gate slow down our releases?

In our MLflow tests the gate added about 154 ms to a promotion outside the trojan scan, and the admission re-check took about 33 ms. Scans run asynchronously and their duration depends on model size, which we measure on your models in the Calibrate phase.

Does this work for large language models?

Lineage, signing, the transparency log and the policy gate apply to models of any size; we have committed a 14 GB checkpoint in about 15 seconds. Trojan inspection and erasure audits for large generative models are an active research area, and we will tell you which guarantees hold for your models before you rely on them.

Is this a compliance certification?

No. We produce evidence that maps to EU AI Act, GDPR, NIST AI RMF and ISO/IEC 42001 obligations. Certification and legal opinions come from your auditors and advisers, who can verify that evidence independently.

Prove every model you ship.

Who is reading about AI supply-chain security

Your models pass through more hands than your code review ever sees.

Data sourcing

Training

Model registry

Post-release edits

Deployment

An assurance control plane that sits beside the MLOps stack you already run.

A team asks to promote a model version

The gate gathers evidence

Lineage is verified back to registered roots

The model is scanned for trojans

Erasure claims are audited

The tier's policy is applied

A signed certificate is logged

Release, review or block

Why a single flipped bit cannot hide

What your security team sees every day

Release decisions over time

Alert feed

Open reviews

Models in production

Gate calibration

Detector coverage by attack family

What we deliver

AI Supply-Chain Security Assessment

Post-Quantum Cryptography Migration

GenAI & LLM Application Security

Agentic AI Security

AI Red Teaming

Assurance Gate Deployment

Signed Lineage & Transparency Log

Calibrated Trojan Inspection

Machine-Unlearning & Erasure Audits

Compliance Evidence Pack

AI Security Innovation Centre Advisory

AI Security Training for Teams

Records you sign today must still be trusted when quantum computers arrive

The migration clock

Where your AI stack uses vulnerable cryptography

Is your data already at risk?

The disciplines and the stack behind each engagement

Backdoor & trojan detection

Machine unlearning & privacy audits

ML supply-chain cryptography

Statistical assurance

MLOps integration

AI regulation & standards

Post-quantum cryptography

LLM and agentic AI security

Tools and where they apply

Numbers from our research prototype

One detector is never enough

Shadow models must look like real threats

Some unlearning can't be audited from outside

What would the gate flag in your release stream?

Five problems we have worked through in the lab

Stopping tampered weights inside a live model registry

When an attacker knows which detector you use

Checking a GDPR erasure claim with a known error rate

One poisoned dataset, 10,000 deployed models

Verifying every model in a 100,000-model registry

From first workshop to a gate your teams operate

Discover

Integrate

Calibrate

Operate

Evidence your regulators and auditors can check

Research-led AI security from Hyderabad

Start with a supply-chain assessment

Thank you, your enquiry has been sent

We couldn't send it from here