CipherSonic AI is excited to share that Co-founder and CEO Ajay Joshi was recently featured on the Wantrepreneur to Entrepreneur podcast, hosted by Brian Lofrumento. In the episode titled “Revolutionizing Data Security and Nurturing the Next Wave of Cyber Talent,” Ajay delves into the company’s mission to enable secure data processing through fully homomorphic encryption (FHE), allowing computations on encrypted data without decryption.

Ajay discusses his transition from academia to entrepreneurship, highlighting the challenges and opportunities in building a cybersecurity startup. He emphasizes the importance of nurturing talent in the evolving field of cybersecurity and shares insights on balancing his roles as a professor and startup founder. The conversation also touches on the practical applications of FHE and the role of hardware acceleration in making this technology viable.

CipherSonic AI has been selected as a finalist in the 2024 Smart Cities–Smart Bases Innovation Challenge, recognizing breakthrough technologies that enhance security and data resilience in critical infrastructure. CipherSonic’s submission highlighted its cloud-deployable platform for privacy-preserving AI and analytics, leveraging fully homomorphic encryption (FHE) and hardware acceleration to enable computation on encrypted data without ever exposing it.

As a finalist, CipherSonic AI was invited to present at the Defense TechConnect Innovation Summit, engaging directly with stakeholders from the U.S. Department of Defense, smart city planners, and innovation leaders. This recognition reflects CipherSonic’s commitment to advancing post-quantum secure cloud solutions for sectors that demand both high performance and zero-trust data protection.

CipherSonic AI was invited to speak at the 2024 NIST Workshop on Privacy-Enhancing Cryptography (WPEC), where CTO Rashmi Agrawal presented the company’s work on accelerating fully homomorphic encryption (FHE) for secure AI in the cloud. The talk highlighted CipherSonic’s FPGA-optimized backend for performing encrypted computation at scale, enabling data privacy without compromising performance.

The presentation focused on real-world use cases for privacy-preserving analytics in sectors like healthcare, finance, and defense, where sensitive data must remain protected throughout the compute lifecycle. CipherSonic’s participation at WPEC reflects its growing role in shaping the future of secure, post-quantum-ready cloud infrastructure.

CipherSonic AI was named a finalist in the TiE Boston Women – 2024 Global Pitch Competition, a premier platform recognizing high-impact women-led startups across the globe. Co-founder and CTO Rashmi Agrawal presented CipherSonic’s breakthrough in secure AI: a hardware-accelerated platform that enables analytics and machine learning directly on encrypted data using fully homomorphic encryption (FHE).

The competition spotlighted ventures pushing the boundaries of innovation in sectors like cybersecurity, healthtech, and enterprise AI. CipherSonic’s selection as a finalist reflects its leadership in privacy-enhancing technologies and its mission to deliver post-quantum secure cloud solutions for enterprises working with highly sensitive data.

CipherSonic AI has been chosen to participate in MassChallenge’s prestigious US early-stage accelerator program, marking a significant milestone for the company in its journey to advance secure computing solutions. MassChallenge, known for supporting high-impact startups worldwide, selected CipherSonic Labs based on its innovative approach to transforming data privacy and security through cutting-edge technologies.

As a participant in MassChallenge’s accelerator, CipherSonic Labs will benefit from mentorship, resources, and strategic guidance aimed at accelerating its growth and market impact. The program provides access to a robust network of industry experts, investors, and potential partners, fostering opportunities for collaboration and business development.

CipherSonic AI took center stage at the Confidential Computing Summit 2024 in San Francisco, leveraging the prestigious event not only to showcase its cutting-edge technologies but also to engage in crucial customer discovery initiatives. The summit, renowned for its focus on advancing secure data processing solutions, provided CipherSonic AI with a unique platform to interact directly with industry leaders, stakeholders, and potential customers.

CipherSonic AI utilized the summit to deepen its understanding of market needs and gather valuable feedback on its innovative solutions. Throughout the summit, CipherSonic AI engaged in meaningful discussions during keynote addresses, panel sessions, and networking opportunities. The company highlighted its expertise in leveraging hardware-based security technologies to enhance data protection and compliance with stringent privacy regulations.

CipherSonic AI has been accepted into the highly competitive Summer Accelerator program at BUild Lab, Boston University’s hub for innovation and entrepreneurship. The BUild Lab Summer Accelerator program is designed to support early-stage startups in scaling their ventures through mentorship, resources, and strategic guidance. Participants benefit from access to BUild Lab’s extensive network of mentors, investors, and industry experts.

As CipherSonic AI embarks on this transformative journey with BUild Lab, the company remains committed to pushing the boundaries of secure computing technologies. With a strong emphasis on research and development, CipherSonic continues to lead the charge in redefining standards for data privacy and security in the digital era.

CipherSonic AI has been accepted into the highly competitive Summer Accelerator program at BUild Lab, Boston University’s hub for innovation and entrepreneurship. The BUild Lab Summer Accelerator program is designed to support early-stage startups in scaling their ventures through mentorship, resources, and strategic guidance. Participants benefit from access to BUild Lab’s extensive network of mentors, investors, and industry experts.

As CipherSonic AI embarks on this transformative journey with BUild Lab, the company remains committed to pushing the boundaries of secure computing technologies. With a strong emphasis on research and development, CipherSonic continues to lead the charge in redefining standards for data privacy and security in the digital era.

CipherSonic AI has proudly secured a spot as a finalist in Boston University’s prestigious New Venture competition, where ten student teams competed for a total of $72,000 in funding. CipherSonic AI, renowned for its pioneering work in fully homomorphic encryption (FHE) technologies, impressed judges with its innovative solution aimed at revolutionizing data privacy and security in digital environments.

“We are thrilled to be selected as a finalist for Innovators Night at Boston University,” said Dr. Ajay Joshi, CEO of CipherSonic AI. “This competition is an excellent opportunity for us to showcase our groundbreaking technologies that enable computations on encrypted data, ensuring privacy without compromising utility.”

CipherSonic AI was honored to participate in the Cyber Venture Forum 2025, held on February 19 at Capital One Hall in Tysons, Virginia. Hosted by Blu Venture Investors, this premier cybersecurity event brought together over 400 participants, including more than 300 companies, with 75% executive presence.

At the forum, CipherSonic AI presented its cutting-edge solutions in fully homomorphic encryption (FHE) and post-quantum secure AI infrastructure, highlighting how its cloud-native platform enables encrypted computation for sensitive data. The event featured personalized matchmaking, founders and funders pitch sessions, and the Cyber Tank Pitch Competition, providing CipherSonic with valuable opportunities to engage with investors, industry leaders, and potential partners.

CipherSonic AI is excited to announce its participation in the 2025 Bio-IT World Conference & Expo, held April 2–4 in Boston, MA. As part of the event’s poster sessions, the company presented its latest advancements in fully homomorphic encryption (FHE) and post-quantum secure AI infrastructure. The poster detailed CipherSonic’s cloud-native platform that enables encrypted computation for biomedical and clinical data—allowing organizations to perform analytics and machine learning on sensitive datasets without exposing raw information.

The Bio-IT World Conference & Expo is a premier global event that brings together over 3,000 leaders in life sciences, pharmaceuticals, clinical research, healthcare, informatics, and technology. The conference features more than 200 presentations, interactive panels, and an exhibit hall showcasing cutting-edge technologies and solutions. CipherSonic’s participation underscores its commitment to advancing secure, privacy-preserving technologies in the biomedical field.

CipherSonic AI is proud to announce its selection as a finalist at the MassChallenge RESOLVE ‘25 Awards, the culminating event of the 2024 U.S. Early Stage Accelerator Program. This recognition places CipherSonic among a distinguished group of 32 startups chosen from over 1,300 applicants, highlighting the company’s innovative contributions to secure AI and privacy-preserving technologies.

At the RESOLVE ‘25 event held in Boston, CipherSonic showcased its groundbreaking work in fully homomorphic encryption (FHE) and post-quantum cryptography. The company’s solutions enable encrypted data processing in cloud environments, addressing critical needs in sectors such as healthcare, finance, and national security. This achievement underscores CipherSonic’s commitment to advancing data security and its potential to make a significant impact in the cybersecurity landscape.

For more information about the RESOLVE ‘25 Awards and the list of finalists, please visit the MassChallenge website.

CipherSonic AI is proud to share that Co-founder and CTO Rashmi Agrawal has been named to the 2025 “100 Women in AI” list, launched in partnership with Flybridge to celebrate women leading breakthrough innovations in artificial intelligence. This initiative recognizes trailblazers who are not only disrupting the status quo but also shaping the future of technology through purpose-driven innovation.

Rashmi’s selection honors her pioneering work in privacy-preserving AI, specifically in advancing fully homomorphic encryption (FHE) and post-quantum secure cloud infrastructure. Her leadership at CipherSonic AI exemplifies the spirit of the initiative—building impactful, secure, and ethical technologies that redefine what’s possible in enterprise AI.

As cyber threats become more sophisticated, enterprises are rapidly adopting Zero Trust Architecture (ZTA) to secure their networks. The core principle of Zero Trust—never trust, always verify—ensures that no user, device, or system is inherently trusted, even within the corporate perimeter. However, traditional Zero Trust models still face challenges when it comes to secure data processing. This is where Fully Homomorphic Encryption (FHE) enters the picture, revolutionizing enterprise security.

Why Zero Trust Alone Isn’t Enough: While Zero Trust secures data at rest and in transit, data is still exposed during computation. Even with strict access controls, privileged users can become attack vectors. Enterprises increasingly rely on external service providers, requiring a way to process sensitive data securely beyond their direct control.

How FHE Complements Zero Trust: FHE allows enterprises to perform operations on encrypted data without ever decrypting it, ensuring end-to-end data security. Since data remains encrypted throughout processing, even administrators and cloud providers cannot access plaintext information. By keeping sensitive data encrypted at all times, enterprises can meet stringent compliance standards like GDPR, HIPAA, and CCPA more efficiently. Organizations can also run AI models on encrypted datasets, enabling privacy-preserving machine learning without exposing confidential information.

Integrating FHE into Zero Trust frameworks represents the next evolution in cybersecurity. With advancements in FPGA- and GPU-accelerated FHE solutions, enterprises can now achieve scalable, high-performance encrypted computation, making security and usability no longer mutually exclusive.

As cities become smarter, they rely on vast amounts of data to optimize infrastructure, improve public services, and enhance urban living. However, the collection and processing of sensitive data—such as transportation patterns, energy usage, and surveillance feeds—pose serious privacy and security challenges.

Fully Homomorphic Encryption (FHE) offers a groundbreaking solution by enabling computations on encrypted data. This innovation allows smart cities to harness the power of AI and big data analytics while preserving privacy and ensuring compliance with stringent data protection regulations.

How FHE Can Drive Smart City Innovation: Urban planners can perform AI-driven insights on encrypted data, optimizing resource allocation without exposing raw datasets. Devices in smart cities continuously generate data streams—FHE enables secure processing of IoT data in transit and storage. Government agencies can detect anomalies in encrypted financial transactions and surveillance data without compromising citizen privacy.

To unlock the full potential of smart cities, municipalities and technology providers must prioritize privacy-enhancing technologies. Advancements in FPGA- and GPU-accelerated FHE, such as those pioneered by CipherSonic AI, make real-time, privacy-preserving analytics feasible for urban applications. The future of smart cities is data-driven, but it must also be secure.

As artificial intelligence (AI) becomes deeply integrated into critical sectors like healthcare, finance, and government, ensuring data privacy is more important than ever. Traditional encryption secures data at rest and in transit, but data-in-use remains vulnerable—leaving AI models and sensitive information exposed to breaches, insider threats, and regulatory scrutiny.

Fully Homomorphic Encryption (FHE) is a game-changer for AI security, enabling privacy-preserving machine learning (PPML) by allowing computations on encrypted data without decryption. This breakthrough ensures AI can learn and make predictions without ever exposing sensitive data.

Why AI Needs Privacy-Preserving Machine Learning: Stricter data protection laws (e.g., GDPR, CCPA, HIPAA) require companies to protect user data even when processing it. AI models trained on sensitive datasets can become targets for cyberattacks. Privacy concerns often limit AI applications in sensitive domains—FHE enables safe collaboration using encrypted datasets across organizations.

How FHE Enables Secure AI: AI models can be trained and deployed on encrypted data, eliminating exposure risks. Organizations can outsource computation to the cloud without revealing sensitive inputs. AI with FHE benefits healthcare (diagnostics on encrypted medical data), finance (fraud detection without revealing transactions), and national security (intelligence sharing without exposing raw data).

Historically, FHE’s computational overhead made it impractical for real-world AI workloads. However, FPGA-accelerated and GPU-accelerated FHE solutions being developed by CipherSonic AI are bridging the gap—delivering orders-of-magnitude speedups over traditional CPU-based approaches.

In the early days of credit cards, adoption was far from seamless. Transactions were slow, merchants were skeptical, and consumers questioned whether they could trust this new financial model. It wasn’t until the infrastructure matured, standards were established, and trust was built that credit cards became ubiquitous. Fully Homomorphic Encryption (FHE) finds itself at a similar crossroads today.

FHE allows computation on encrypted data without decrypting it—a transformative capability for privacy-preserving AI and secure cloud computing. Yet like credit cards in their early years, FHE faces real barriers to mainstream adoption: computational overhead, lack of developer tooling, and organizational unfamiliarity with the technology.

The path forward for FHE mirrors the credit card journey. Standardization of protocols and APIs will lower the barrier to entry for developers. Hardware acceleration (via FPGAs and GPUs) is rapidly closing the performance gap. And as early adopters in healthcare, finance, and government demonstrate real-world value, trust and demand will grow organically.

CipherSonic AI is working to be the infrastructure layer that makes FHE practical—just as Visa and Mastercard made credit card transactions seamless. The question isn’t whether FHE will become mainstream, but how quickly the ecosystem can build the tools, trust, and talent to make it so.

Data breaches have become a costly epidemic, with enterprises losing millions to security incidents every year. While organizations invest heavily in traditional encryption, these methods often fall short—data is still exposed during processing, leaving a critical vulnerability that attackers and insiders can exploit.

The true cost of a data breach extends far beyond the immediate financial loss. Regulatory fines under GDPR, HIPAA, and CCPA can reach tens of millions of dollars. Reputational damage erodes customer trust and drives churn. Legal fees and remediation costs compound the burden. And for regulated industries like healthcare and finance, operational disruptions can be catastrophic.

Fully Homomorphic Encryption (FHE) addresses this gap by enabling computation on encrypted data—meaning sensitive information never needs to be decrypted, even during processing. The attack surface shrinks dramatically. Insider threats lose their leverage. Cloud providers and third parties cannot access plaintext data.

While FHE has historically carried a performance cost, advances in hardware acceleration at CipherSonic AI are making it practical for enterprise workloads. When weighed against the average cost of a breach—and the compounding costs of non-compliance—investing in FHE infrastructure is not just prudent. It’s essential.

In the realm of secure data processing, Fully Homomorphic Encryption (FHE) has long held promise as a transformative technology, enabling computations on encrypted data without compromising privacy. However, practical adoption has been constrained by significant computational overhead. Recent innovations are rapidly changing this landscape.

Advances in FHE schemes—such as CKKS for approximate arithmetic and TFHE for fast Boolean operations—have expanded the range of workloads that can be executed on encrypted data. Compiler toolchains that automatically translate standard programs into FHE-compatible circuits are lowering the barrier for developers. And hardware acceleration using FPGAs and GPUs is delivering orders-of-magnitude performance improvements over CPU-based approaches.

CipherSonic AI is at the forefront of these developments, building cloud-native infrastructure that brings FHE performance within reach of real-world enterprise applications. From encrypted AI inference to secure multi-party analytics, the use cases are expanding rapidly as the technology matures.

The convergence of algorithmic improvements, developer tooling, and hardware acceleration signals that FHE is moving from a research curiosity to a production-ready technology—one that will underpin the next generation of privacy-preserving applications across healthcare, finance, and national security.

Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data without having to decrypt it. The computed result, when decrypted, matches exactly what would have been obtained by performing the same operations on the plaintext data. This property makes it enormously powerful for privacy-preserving computation.

Types of Homomorphic Encryption: Partially Homomorphic Encryption (PHE) supports only a single operation (addition or multiplication) an unlimited number of times. Somewhat Homomorphic Encryption (SHE) supports both operations but only a limited number of times before the ciphertext becomes too noisy. Fully Homomorphic Encryption (FHE) supports arbitrary computations on ciphertexts—the most powerful and flexible variant.

Key FHE Schemes: BGV and BFV are optimized for integer arithmetic. CKKS enables approximate computation on real and complex numbers, making it well-suited for machine learning. TFHE supports fast evaluation of Boolean circuits and is used for gate-by-gate computation.

FHE enables a paradigm where cloud providers can process your data without ever seeing it, AI models can be deployed on encrypted inputs, and organizations can collaborate on joint analytics without sharing raw data. CipherSonic AI is building the infrastructure to make these capabilities practical at enterprise scale.

Privacy-enhancing technologies (PETs) encompass a broad range of tools and techniques designed to protect the privacy of individuals and organizations in digital environments. Encryption is fundamental to this ecosystem, but the spectrum of PETs extends far beyond traditional encryption at rest and in transit.

Key Privacy-Enhancing Technologies: Differential Privacy adds calibrated statistical noise to data queries, preventing individual records from being identified. Secure Multi-Party Computation (SMPC) allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. Zero-Knowledge Proofs enable one party to prove knowledge of a value without revealing the value itself. And Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data—the most powerful and general-purpose PET available.

The Evolution of FHE: The concept of fully homomorphic encryption was first proposed by Craig Gentry in 2009. Early implementations were theoretically elegant but computationally impractical. Over the following decade, a succession of improved schemes (BGV, BFV, CKKS, TFHE) dramatically reduced the computational cost of FHE operations. Today, hardware acceleration using FPGAs and specialized ASICs is pushing FHE performance into the range needed for real-world enterprise workloads.

CipherSonic AI is building at the frontier of this evolution, combining state-of-the-art FHE schemes with hardware acceleration and post-quantum cryptographic security to deliver privacy-preserving computation infrastructure for the enterprise.

Recent advancements in quantum computing indicate an inevitable future of functional and widely accessible quantum computer systems. Progress has been notable in both the number of qubits available and the error correction techniques needed to make quantum computers practical. This trajectory poses a serious threat to the cryptographic foundations that currently secure the internet, financial systems, and sensitive government communications.

The Quantum Threat: Most public-key cryptography in use today—including RSA, ECC, and Diffie-Hellman key exchange—is vulnerable to Shor’s algorithm, which can run efficiently on a sufficiently powerful quantum computer. This means that a future quantum adversary could retroactively decrypt communications captured today, a threat known as “harvest now, decrypt later.”

NIST Post-Quantum Standards: The U.S. National Institute of Standards and Technology (NIST) has been leading a multi-year process to standardize post-quantum cryptographic algorithms. In 2024, NIST finalized its first set of post-quantum standards, including CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, both based on the hardness of lattice problems.

CipherSonic AI integrates post-quantum cryptographic primitives into its FHE infrastructure, ensuring that the security guarantees of our platform remain robust against both classical and quantum adversaries. Organizations that need to protect sensitive data with long-term confidentiality requirements should begin their post-quantum migration planning now.